knit_as_emar()

Introduction

In this exploratory multiverse analysis report, we implement a specification curve analysis, to answer the research question: whether hurricanes with more feminine names have caused more deaths compared to hurricanes with more masculine names. The original paper found that hurricanes with more feminine names did cause more deaths. However, this paper led to an intense debate about the proper way to analyse the underlying data, providing an opportunity to assess the extent to which the actual outcome is sensitive to arbitrary decisions in the data analysis process.

A specification curve analysis is in principle similar to a multiverse analysis, where all alternate specifications of a particular analysis asking the same research question are explored. In their study, Simonsohn et al. explore the robustness of the analysis by Jung et al. [https://doi.org/10.1073/pnas.1402786111], which investigated whether hurricanes with female sounding names are more deadlier than hurricanes with more male sounding names. We first begin by loading the dataset which is provided by the library. We then rename some of the variables and perform some data transformations which standardises some of the variables (mean = 0 and standard deviation = 1).

data("hurricane")

# read and process data
hurricane_data <- hurricane %>%
    # rename some variables
    rename(
        year = Year,
        name = Name,
        dam = NDAM,
        death = alldeaths,
        female = Gender_MF,
        masfem = MasFem,
        category = Category,
        pressure = Minpressure_Updated_2014,
        wind = HighestWindSpeed
    ) %>%
    # create new variables
    mutate(
        post = ifelse(year>1979, 1, 0),
        zdam = scale(dam),
        zcat = as.numeric(scale(category)),
        zpressure = -scale(pressure),
        zwind = as.numeric(scale(wind)),
        z3 = as.numeric((zpressure + zcat + zwind) / 3)
    )

Original analysis

Before we implement the multiverse analysis, we illustrate an implementation of the original analysis by Jung et al. [https://doi.org/10.1073/pnas.1402786111]. The original analysis used a negative binomial model, which is suitable for overdispersed count data. Due to some issues with model fit with the MASS::glm.nb function (see Note 3: https://github.com/uwdata/boba/tree/master/example/hurricane), we instead use the simpler poisson regression model which will ensure that none of the models fail while fitting.

In the original analysis, Jung et al. exclude two hurricanes which caused the highest number of deaths (Katrina and Audrey) as outliers. They transform the variable used the interactions between the 11-point femininity rating and both damages and zpressure respectively, as seen below:

df <- hurricane_data %>%
    filter( name != "Katrina" & name != "Audrey" )

fit <- glm(death ~ masfem * zdam + masfem * zpressure, data = df, family = "poisson")

summary(fit)
## 
## Call:
## glm(formula = death ~ masfem * zdam + masfem * zpressure, family = "poisson", 
##     data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -14.4855   -3.5404   -2.4125    0.5033   18.5521  
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      2.333437   0.076926  30.334  < 2e-16 ***
## masfem           0.059789   0.010545   5.670 1.43e-08 ***
## zdam             0.439454   0.076209   5.766 8.10e-09 ***
## zpressure        0.143637   0.106263   1.352  0.17647    
## masfem:zdam      0.024825   0.009495   2.614  0.00894 ** 
## masfem:zpressure 0.026602   0.013167   2.020  0.04334 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4031.9  on 91  degrees of freedom
## Residual deviance: 2133.2  on 86  degrees of freedom
## AIC: 2470.1
## 
## Number of Fisher Scoring iterations: 6

The results above indicate that the femininity of the name of the hurricane (masfem) does have a statistically significant effect on deaths. Below, we visualise the expected number of deaths as the femininity of the name of the hurricane increases. From this, it seems to suggest that the most feminine hurricane will likely lead to 0.5 extra deaths on average.

data_grid(df, masfem = seq(1, 11, by = 0.2), zdam, zpressure) %>%
  broom::augment(fit, newdata = ., se_fit = TRUE) %>%
  ggplot(aes(x = masfem, y = .fitted)) +
  stat_lineribbon() +
  scale_fill_brewer() +
  scale_x_continuous(breaks = seq(1, 11, by = 2)) +
  theme_minimal() +
  labs(x = "masculine-feminine rating (11 point likert scale)", y = "expected number of deaths")

Multiverse Analysis

To implement a multiverse analysis, we first need to create the multiverse object:

M <- multiverse()

Excluding outliers

In the original analysis, the authors exclude two most extreme observations based on the number of deaths. However, this appears to be an arbitrary choice, especially considering the use of a negative binomial regression model, which accounts for long-tailed distribution of the outcome variable (death). In their implementation, Simonsohn et al. describe a principled method of excluding outliers based on extreme observations of death and damages. They consider it reasonable to exclude up two most extreme hurricanes in terms of death, and upto three most extreme hurricanes in terms of damages. We implement these decisions in our multiverse using the following two parameters:

df <- hurricane_data %>%
    filter(TRUE) %>%
    filter(TRUE)
df <- hurricane_data %>%
    filter(TRUE) %>%
    filter(!(name %in% c("Sandy")))
df <- hurricane_data %>%
    filter(TRUE) %>%
    filter(!(name %in% c("Sandy", "Andrew")))
df <- hurricane_data %>%
    filter(TRUE) %>%
    filter(!(name %in% c("Sandy", "Andrew", "Donna")))
df <- hurricane_data %>%
    filter(name != "Katrina") %>%
    filter(TRUE)
df <- hurricane_data %>%
    filter(name != "Katrina") %>%
    filter(!(name %in% c("Sandy")))
df <- hurricane_data %>%
    filter(name != "Katrina") %>%
    filter(!(name %in% c("Sandy", "Andrew")))
df <- hurricane_data %>%
    filter(name != "Katrina") %>%
    filter(!(name %in% c("Sandy", "Andrew", "Donna")))
df <- hurricane_data %>%
    filter(!(name %in% c("Katrina", "Audrey"))) %>%
    filter(TRUE)
df <- hurricane_data %>%
    filter(!(name %in% c("Katrina", "Audrey"))) %>%
    filter(!(name %in% c("Sandy")))
df <- hurricane_data %>%
    filter(!(name %in% c("Katrina", "Audrey"))) %>%
    filter(!(name %in% c("Sandy", "Andrew")))
df <- hurricane_data %>%
    filter(!(name %in% c("Katrina", "Audrey"))) %>%
    filter(!(name %in% c("Sandy", "Andrew", "Donna")))
df <- hurricane_data %>%
    filter(branch(death_outliers, 
        "no_exclusion" ~ TRUE,
        "one_most_extreme_deaths" ~ name != "Katrina",
        "two_most_extreme_deaths" ~ ! (name %in% c("Katrina", "Audrey"))
    )) %>%
    filter(branch(damage_outliers,
        "no_exclusion" ~ TRUE,
        "one_most_extreme_damage" ~ ! (name %in% c("Sandy")),
        "two_most_extreme_damage" ~ ! (name %in% c("Sandy", "Andrew")),
        "three_most_extreme_damage" ~ ! (name %in% c("Sandy", "Andrew", "Donna"))
    ))

Identifying independent variables

The next decision involves identifying the appropriate independent variable for the primary effect — how do we operationalise femininity of the name of a hurricane. Simonsohn et al. identify two distinct ways. First, using the 11 point scale that was used in the original analysis; or second using a binary scale. In our multiverse, this decision is parameterised by:

df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(femininity = masfem)
df <- df %>%
    mutate(femininity = female)
df <- df %>%
    mutate(
        femininity = branch(femininity_calculation,
          "masfem" ~ masfem,
          "female" ~ female
    ))

The damages follow a long tailed, positive only valued distribution. Thus, the other decision involved is whether or not to transform damages, another independent variable:

df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
    mutate(, damage = identity(dam))
df = df %>%
    mutate(, damage = log(dam))
df = df %>%
  mutate(,
        damage = branch(damage_transform,
          "no_transform" ~ identity(dam),
          "log_transform" ~ log(dam)
  ))

Declaring alternative specifications of regression model

The next step is to fit the model. We use a linear model for our analysis, and log-transform the outcome variable deaths because it is a long-tailed distribution. Our first decision involves whether we want to include an interaction between femininity and damage, which is given by (for the sake of simplicity, we omit the zpressure covariates from this multiverse analysis).

Another decisions involves whether we should control for the year in which the hurricane occured, as hurricane detection and preparedness provisions may likely to have improved over the years. In addition, there’s a discontinuity in 1979, as prior to 1979 all hurricanes had male names. This manifests as a decisions with three options: not controlling for year, controlling for the interaction between year and damage, and controlling for interaction between whether the hurricane was post 1979 (post) and damage. These decisions are given by

fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7739 -0.8214 -0.2298  0.8971  4.1454 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.376e+00  2.948e-01   4.668 1.04e-05 ***
femininity  3.421e-02  4.129e-02   0.829     0.41    
damage      4.583e-05  5.256e-06   8.720 1.22e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.142 on 91 degrees of freedom
Multiple R-squared:  0.4608,    Adjusted R-squared:  0.449 
F-statistic: 38.89 on 2 and 91 DF,  p-value: 6.219e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0411 -0.7835 -0.2199  0.8425  4.1349 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.393e+00  2.947e-01   4.727 8.39e-06 ***
femininity   2.775e-02  4.161e-02   0.667    0.507    
damage       5.967e-04  4.839e-04   1.233    0.221    
damage:year -2.762e-07  2.426e-07  -1.138    0.258    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.14 on 90 degrees of freedom
Multiple R-squared:  0.4685,    Adjusted R-squared:  0.4508 
F-statistic: 26.44 on 3 and 90 DF,  p-value: 2.353e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3700 -0.7951 -0.2054  0.8867  4.1407 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.422e+00  2.938e-01   4.841 5.34e-06 ***
femininity   2.161e-02  4.171e-02   0.518    0.606    
damage       5.990e-05  1.029e-05   5.822 8.83e-08 ***
damage:post -1.756e-05  1.107e-05  -1.586    0.116    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.133 on 90 degrees of freedom
Multiple R-squared:  0.4755,    Adjusted R-squared:  0.458 
F-statistic:  27.2 on 3 and 90 DF,  p-value: 1.304e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7697 -0.8316 -0.2388  0.8811  4.1509 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.413e+00  3.345e-01   4.224 5.72e-05 ***
femininity        2.898e-02  4.698e-02   0.617  0.53890    
damage            4.270e-05  1.417e-05   3.014  0.00335 ** 
femininity:damage 4.248e-07  1.786e-06   0.238  0.81249    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.148 on 90 degrees of freedom
Multiple R-squared:  0.4612,    Adjusted R-squared:  0.4432 
F-statistic: 25.68 on 3 and 90 DF,  p-value: 4.321e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0806 -0.8054 -0.2238  0.8442  4.1404 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.429e+00  3.343e-01   4.275 4.78e-05 ***
femininity         2.261e-02  4.724e-02   0.479    0.633    
damage             5.932e-04  4.867e-04   1.219    0.226    
femininity:damage  4.176e-07  1.783e-06   0.234    0.815    
damage:year       -2.760e-07  2.439e-07  -1.132    0.261    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.146 on 89 degrees of freedom
Multiple R-squared:  0.4688,    Adjusted R-squared:  0.4449 
F-statistic: 19.64 on 4 and 89 DF,  p-value: 1.298e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3676 -0.7910 -0.2054  0.8903  4.1396 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.415e+00  3.318e-01   4.265 4.96e-05 ***
femininity         2.253e-02  4.679e-02   0.482  0.63128    
damage             6.057e-05  1.813e-05   3.340  0.00122 ** 
femininity:damage -8.065e-08  1.801e-06  -0.045  0.96438    
damage:post       -1.766e-05  1.132e-05  -1.560  0.12237    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 89 degrees of freedom
Multiple R-squared:  0.4755,    Adjusted R-squared:  0.4519 
F-statistic: 20.17 on 4 and 89 DF,  p-value: 7.485e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3404 -0.8299 -0.0068  0.5712  3.4270 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.39196    0.42820  -3.251  0.00161 ** 
femininity   0.02624    0.03919   0.669  0.50494    
damage       0.44562    0.04576   9.737 9.04e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.083 on 91 degrees of freedom
Multiple R-squared:  0.5153,    Adjusted R-squared:  0.5047 
F-statistic: 48.38 on 2 and 91 DF,  p-value: 4.881e-15
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2783 -0.8634 -0.0316  0.6305  3.4874 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.4024571  0.4308658  -3.255   0.0016 **
femininity   0.0301059  0.0404269   0.745   0.4584   
damage      -0.1757666  1.4741483  -0.119   0.9054   
damage:year  0.0003125  0.0007410   0.422   0.6742   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.088 on 90 degrees of freedom
Multiple R-squared:  0.5163,    Adjusted R-squared:  0.5001 
F-statistic: 32.02 on 3 and 90 DF,  p-value: 3.553e-14
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3304 -0.8398 -0.0092  0.5808  3.4374 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.395944   0.432872  -3.225  0.00176 ** 
femininity   0.027144   0.040707   0.667  0.50661    
damage       0.443941   0.049716   8.930 4.82e-14 ***
damage:post  0.002613   0.029358   0.089  0.92929    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.089 on 90 degrees of freedom
Multiple R-squared:  0.5154,    Adjusted R-squared:  0.4992 
F-statistic:  31.9 on 3 and 90 DF,  p-value: 3.865e-14
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3433 -0.8061  0.0404  0.6091  3.3853 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.25298    0.84232  -0.300  0.76461   
femininity        -0.16079    0.12561  -1.280  0.20378   
damage             0.29328    0.10735   2.732  0.00758 **
femininity:damage  0.02474    0.01580   1.566  0.12087   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.074 on 90 degrees of freedom
Multiple R-squared:  0.5282,    Adjusted R-squared:  0.5124 
F-statistic: 33.58 on 3 and 90 DF,  p-value: 1.171e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2606 -0.7663  0.0242  0.6154  3.4644 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.2306559  0.8464489  -0.272    0.786
femininity        -0.1616008  0.1260921  -1.282    0.203
damage            -0.5392979  1.4789253  -0.365    0.716
femininity:damage  0.0255254  0.0159183   1.604    0.112
damage:year        0.0004163  0.0007375   0.564    0.574

Residual standard error: 1.078 on 89 degrees of freedom
Multiple R-squared:  0.5299,    Adjusted R-squared:  0.5087 
F-statistic: 25.08 on 4 and 89 DF,  p-value: 6.382e-14
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3165 -0.7780  0.0425  0.6089  3.4126 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)       -0.246742   0.847159  -0.291   0.7715  
femininity        -0.161136   0.126276  -1.276   0.2053  
damage             0.286501   0.111542   2.569   0.0119 *
femininity:damage  0.025105   0.015954   1.574   0.1191  
damage:post        0.007033   0.029255   0.240   0.8106  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.08 on 89 degrees of freedom
Multiple R-squared:  0.5285,    Adjusted R-squared:  0.5073 
F-statistic: 24.94 on 4 and 89 DF,  p-value: 7.251e-14
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7967 -0.7487 -0.2783  0.9239  4.1428 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.434e+00  2.156e-01   6.650 2.13e-09 ***
femininity  2.386e-01  2.527e-01   0.944    0.348    
damage      4.586e-05  5.247e-06   8.740 1.10e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.141 on 91 degrees of freedom
Multiple R-squared:  0.462, Adjusted R-squared:  0.4502 
F-statistic: 39.08 on 2 and 91 DF,  p-value: 5.618e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0265 -0.7478 -0.2476  0.8230  4.1316 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.437e+00  2.153e-01   6.673 1.99e-09 ***
femininity   1.983e-01  2.549e-01   0.778    0.438    
damage       5.877e-04  4.838e-04   1.215    0.228    
damage:year -2.717e-07  2.425e-07  -1.120    0.266    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 90 degrees of freedom
Multiple R-squared:  0.4694,    Adjusted R-squared:  0.4517 
F-statistic: 26.54 on 3 and 90 DF,  p-value: 2.174e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3530 -0.7644 -0.2290  0.8730  4.1365 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.453e+00  2.143e-01   6.780 1.22e-09 ***
femininity   1.603e-01  2.557e-01   0.627    0.532    
damage       5.970e-05  1.028e-05   5.808 9.41e-08 ***
damage:post -1.729e-05  1.108e-05  -1.561    0.122    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.132 on 90 degrees of freedom
Multiple R-squared:  0.4762,    Adjusted R-squared:  0.4588 
F-statistic: 27.28 on 3 and 90 DF,  p-value: 1.226e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7840 -0.7923 -0.2730  0.8890  4.1529 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.478e+00  2.392e-01   6.180 1.83e-08 ***
femininity        1.781e-01  2.891e-01   0.616 0.539510    
damage            4.169e-05  1.091e-05   3.819 0.000246 ***
femininity:damage 5.442e-06  1.246e-05   0.437 0.663458    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.146 on 90 degrees of freedom
Multiple R-squared:  0.4632,    Adjusted R-squared:  0.4453 
F-statistic: 25.88 on 3 and 90 DF,  p-value: 3.662e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0902 -0.7869 -0.2383  0.8088  4.1408 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.476e+00  2.389e-01   6.179  1.9e-08 ***
femininity         1.450e-01  2.904e-01   0.499    0.619    
damage             5.759e-04  4.871e-04   1.182    0.240    
femininity:damage  4.855e-06  1.246e-05   0.390    0.698    
damage:year       -2.676e-07  2.439e-07  -1.097    0.276    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 89 degrees of freedom
Multiple R-squared:  0.4703,    Adjusted R-squared:  0.4465 
F-statistic: 19.76 on 4 and 89 DF,  p-value: 1.147e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3562 -0.7727 -0.2273  0.8698  4.1385 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.461e+00  2.379e-01   6.143 2.22e-08 ***
femininity         1.494e-01  2.878e-01   0.519 0.605055    
damage             5.870e-05  1.574e-05   3.730 0.000337 ***
femininity:damage  1.069e-06  1.272e-05   0.084 0.933233    
damage:post       -1.707e-05  1.145e-05  -1.491 0.139388    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.138 on 89 degrees of freedom
Multiple R-squared:  0.4763,    Adjusted R-squared:  0.4527 
F-statistic: 20.23 on 4 and 89 DF,  p-value: 7.032e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3256 -0.8221 -0.0162  0.5876  3.4571 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.28217    0.38504  -3.330  0.00126 ** 
femininity   0.08066    0.24148   0.334  0.73914    
damage       0.44621    0.04600   9.701 1.08e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.085 on 91 degrees of freedom
Multiple R-squared:  0.5135,    Adjusted R-squared:  0.5028 
F-statistic: 48.03 on 2 and 91 DF,  p-value: 5.772e-15
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2742 -0.8603 -0.0249  0.6336  3.5091 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.2846554  0.3869543  -3.320   0.0013 **
femininity   0.1051797  0.2519314   0.417   0.6773   
damage      -0.0940736  1.4943910  -0.063   0.9499   
damage:year  0.0002716  0.0007509   0.362   0.7184   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.09 on 90 degrees of freedom
Multiple R-squared:  0.5142,    Adjusted R-squared:  0.498 
F-statistic: 31.76 on 3 and 90 DF,  p-value: 4.286e-14
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3230 -0.8256 -0.0161  0.5874  3.4599 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.2828484  0.3881359  -3.305  0.00136 ** 
femininity   0.0826413  0.2556799   0.323  0.74728    
damage       0.4457120  0.0503817   8.847 7.18e-14 ***
damage:post  0.0007433  0.0299840   0.025  0.98028    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.091 on 90 degrees of freedom
Multiple R-squared:  0.5135,    Adjusted R-squared:  0.4973 
F-statistic: 31.67 on 3 and 90 DF,  p-value: 4.571e-14
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3342 -0.7957  0.0148  0.6053  3.4175 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.63652    0.56660  -1.123    0.264    
femininity        -0.99270    0.73557  -1.350    0.181    
damage             0.35627    0.07402   4.813 5.96e-06 ***
femininity:damage  0.14514    0.09403   1.543    0.126    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.077 on 90 degrees of freedom
Multiple R-squared:  0.5261,    Adjusted R-squared:  0.5103 
F-statistic:  33.3 on 3 and 90 DF,  p-value: 1.428e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2635 -0.7675  0.0169  0.6271  3.4882 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.6214653  0.5697617  -1.091    0.278
femininity        -0.9895951  0.7386754  -1.340    0.184
damage            -0.3914507  1.4942281  -0.262    0.794
femininity:damage  0.1492900  0.0947914   1.575    0.119
damage:year        0.0003746  0.0007476   0.501    0.618

Residual standard error: 1.081 on 89 degrees of freedom
Multiple R-squared:  0.5274,    Adjusted R-squared:  0.5062 
F-statistic: 24.83 on 4 and 89 DF,  p-value: 8.009e-14
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3167 -0.7826  0.0102  0.6161  3.4366 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.634498   0.569807  -1.114    0.268    
femininity        -0.990241   0.739717  -1.339    0.184    
damage             0.351996   0.078636   4.476 2.24e-05 ***
femininity:damage  0.146620   0.094957   1.544    0.126    
damage:post        0.005029   0.029885   0.168    0.867    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.083 on 89 degrees of freedom
Multiple R-squared:  0.5262,    Adjusted R-squared:  0.5049 
F-statistic: 24.71 on 4 and 89 DF,  p-value: 8.931e-14
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7737 -0.8308 -0.2148  0.9130  4.1445 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.372e+00  2.969e-01   4.621 1.27e-05 ***
femininity  3.457e-02  4.154e-02   0.832    0.407    
damage      4.622e-05  5.562e-06   8.311 9.30e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.148 on 90 degrees of freedom
Multiple R-squared:  0.4391,    Adjusted R-squared:  0.4266 
F-statistic: 35.23 on 2 and 90 DF,  p-value: 5.019e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0564 -0.7924 -0.2533  0.8538  4.1350 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.397e+00  2.973e-01   4.699 9.46e-06 ***
femininity   2.709e-02  4.201e-02   0.645    0.521    
damage       6.274e-04  5.174e-04   1.213    0.228    
damage:year -2.917e-07  2.597e-07  -1.123    0.264    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.146 on 89 degrees of freedom
Multiple R-squared:  0.4469,    Adjusted R-squared:  0.4283 
F-statistic: 23.97 on 3 and 89 DF,  p-value: 1.842e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3698 -0.8015 -0.2267  0.9003  4.1410 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.424e+00  2.964e-01   4.805 6.24e-06 ***
femininity   2.133e-02  4.207e-02   0.507    0.613    
damage       5.990e-05  1.035e-05   5.790 1.04e-07 ***
damage:post -1.776e-05  1.136e-05  -1.564    0.121    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 89 degrees of freedom
Multiple R-squared:  0.4541,    Adjusted R-squared:  0.4357 
F-statistic: 24.68 on 3 and 89 DF,  p-value: 1.04e-11
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7693 -0.8359 -0.2179  0.8908  4.1504 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.411e+00  3.364e-01   4.194 6.46e-05 ***
femininity        2.905e-02  4.723e-02   0.615  0.53999    
damage            4.294e-05  1.428e-05   3.007  0.00343 ** 
femininity:damage 4.495e-07  1.798e-06   0.250  0.80316    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.154 on 89 degrees of freedom
Multiple R-squared:  0.4395,    Adjusted R-squared:  0.4206 
F-statistic: 23.26 on 3 and 89 DF,  p-value: 3.313e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0930 -0.8122 -0.2511  0.8556  4.1402 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.431e+00  3.364e-01   4.255 5.21e-05 ***
femininity         2.222e-02  4.756e-02   0.467    0.641    
damage             6.215e-04  5.208e-04   1.193    0.236    
femininity:damage  3.996e-07  1.796e-06   0.222    0.824    
damage:year       -2.903e-07  2.612e-07  -1.111    0.269    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.153 on 88 degrees of freedom
Multiple R-squared:  0.4472,    Adjusted R-squared:  0.4221 
F-statistic:  17.8 on 4 and 88 DF,  p-value: 9.714e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3670 -0.7968 -0.2274  0.9049  4.1397 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.416e+00  3.338e-01   4.242 5.45e-05 ***
femininity         2.242e-02  4.707e-02   0.476  0.63498    
damage             6.070e-05  1.830e-05   3.318  0.00132 ** 
femininity:damage -9.656e-08  1.820e-06  -0.053  0.95780    
damage:post       -1.788e-05  1.165e-05  -1.535  0.12834    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 88 degrees of freedom
Multiple R-squared:  0.4541,    Adjusted R-squared:  0.4293 
F-statistic:  18.3 on 4 and 88 DF,  p-value: 5.689e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3210 -0.8340  0.0072  0.5928  3.4522 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.33041    0.43070  -3.089  0.00267 ** 
femininity   0.02429    0.03916   0.620  0.53669    
damage       0.43735    0.04623   9.460  3.8e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.081 on 90 degrees of freedom
Multiple R-squared:  0.5029,    Adjusted R-squared:  0.4918 
F-statistic: 45.52 on 2 and 90 DF,  p-value: 2.191e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2971 -0.8400 -0.0106  0.6136  3.4752 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.3365956  0.4347270  -3.075   0.0028 **
femininity   0.0258772  0.0405764   0.638   0.5253   
damage       0.1924124  1.5114623   0.127   0.8990   
damage:year  0.0001233  0.0007606   0.162   0.8716   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.087 on 89 degrees of freedom
Multiple R-squared:  0.503, Adjusted R-squared:  0.4863 
F-statistic: 30.03 on 3 and 89 DF,  p-value: 1.669e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3297 -0.8264  0.0018  0.5816  3.4432 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.326229   0.436329  -3.040  0.00311 ** 
femininity   0.023454   0.040761   0.575  0.56647    
damage       0.438765   0.049830   8.805 9.51e-14 ***
damage:post -0.002335   0.029619  -0.079  0.93734    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.087 on 89 degrees of freedom
Multiple R-squared:  0.5029,    Adjusted R-squared:  0.4862 
F-statistic: 30.01 on 3 and 89 DF,  p-value: 1.686e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3259 -0.8145  0.0182  0.5990  3.4101 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.26721    0.84219  -0.317  0.75178   
femininity        -0.15125    0.12591  -1.201  0.23287   
damage             0.29515    0.10734   2.750  0.00722 **
femininity:damage  0.02325    0.01586   1.466  0.14623   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.074 on 89 degrees of freedom
Multiple R-squared:  0.5146,    Adjusted R-squared:  0.4982 
F-statistic: 31.45 on 3 and 89 DF,  p-value: 5.915e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2776 -0.7675  0.0197  0.6000  3.4557 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.2526571  0.8475970  -0.298    0.766
femininity        -0.1524914  0.1266069  -1.204    0.232
damage            -0.2053734  1.5249762  -0.135    0.893
femininity:damage  0.0238386  0.0160405   1.486    0.141
damage:year        0.0002502  0.0007603   0.329    0.743

Residual standard error: 1.079 on 88 degrees of freedom
Multiple R-squared:  0.5152,    Adjusted R-squared:  0.4932 
F-statistic: 23.38 on 4 and 88 DF,  p-value: 3.46e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3168 -0.8166  0.0078  0.5915  3.4193 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)       -0.264845   0.847408  -0.313   0.7554  
femininity        -0.151488   0.126657  -1.196   0.2349  
damage             0.292762   0.111726   2.620   0.0103 *
femininity:damage  0.023394   0.016047   1.458   0.1485  
damage:post        0.002457   0.029617   0.083   0.9341  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.08 on 88 degrees of freedom
Multiple R-squared:  0.5146,    Adjusted R-squared:  0.4926 
F-statistic: 23.33 on 4 and 88 DF,  p-value: 3.636e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7971 -0.7525 -0.2857  0.9493  4.1415 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.429e+00  2.175e-01   6.571 3.17e-09 ***
femininity  2.424e-01  2.545e-01   0.953    0.343    
damage      4.629e-05  5.552e-06   8.338 8.15e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.147 on 90 degrees of freedom
Multiple R-squared:  0.4404,    Adjusted R-squared:  0.428 
F-statistic: 35.42 on 2 and 90 DF,  p-value: 4.512e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0396 -0.7512 -0.2511  0.8285  4.1319 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.440e+00  2.175e-01   6.620 2.63e-09 ***
femininity   1.940e-01  2.580e-01   0.752    0.454    
damage       6.136e-04  5.180e-04   1.184    0.239    
damage:year -2.848e-07  2.600e-07  -1.095    0.276    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.146 on 89 degrees of freedom
Multiple R-squared:  0.4478,    Adjusted R-squared:  0.4292 
F-statistic: 24.06 on 3 and 89 DF,  p-value: 1.711e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3531 -0.7664 -0.2298  0.8831  4.1367 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.454e+00  2.165e-01   6.716 1.70e-09 ***
femininity   1.586e-01  2.584e-01   0.614    0.541    
damage       5.970e-05  1.034e-05   5.776 1.11e-07 ***
damage:post -1.744e-05  1.137e-05  -1.533    0.129    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.138 on 89 degrees of freedom
Multiple R-squared:  0.4548,    Adjusted R-squared:  0.4364 
F-statistic: 24.75 on 3 and 89 DF,  p-value: 9.802e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7828 -0.7934 -0.2670  0.9084  4.1526 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.478e+00  2.404e-01   6.149 2.16e-08 ***
femininity        1.748e-01  2.908e-01   0.601 0.549312    
damage            4.169e-05  1.097e-05   3.800 0.000264 ***
femininity:damage 6.211e-06  1.274e-05   0.488 0.627034    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.152 on 89 degrees of freedom
Multiple R-squared:  0.4419,    Adjusted R-squared:  0.4231 
F-statistic: 23.49 on 3 and 89 DF,  p-value: 2.739e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0940 -0.7873 -0.2411  0.8138  4.1406 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.476e+00  2.403e-01   6.144 2.28e-08 ***
femininity         1.449e-01  2.920e-01   0.496    0.621    
damage             5.882e-04  5.252e-04   1.120    0.266    
femininity:damage  4.680e-06  1.282e-05   0.365    0.716    
damage:year       -2.738e-07  2.630e-07  -1.041    0.301    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.151 on 88 degrees of freedom
Multiple R-squared:  0.4487,    Adjusted R-squared:  0.4236 
F-statistic:  17.9 on 4 and 88 DF,  p-value: 8.681e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3559 -0.7733 -0.2294  0.8795  4.1385 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.461e+00  2.392e-01   6.108 2.68e-08 ***
femininity         1.496e-01  2.895e-01   0.517 0.606611    
damage             5.883e-05  1.611e-05   3.651 0.000442 ***
femininity:damage  9.304e-07  1.318e-05   0.071 0.943868    
damage:post       -1.721e-05  1.190e-05  -1.445 0.151914    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 88 degrees of freedom
Multiple R-squared:  0.4548,    Adjusted R-squared:  0.4301 
F-statistic: 18.35 on 4 and 88 DF,  p-value: 5.366e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3042 -0.8030 -0.0323  0.6234  3.4831 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.22331    0.38758  -3.156  0.00217 ** 
femininity   0.06521    0.24136   0.270  0.78765    
damage       0.43801    0.04644   9.432 4.34e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.083 on 90 degrees of freedom
Multiple R-squared:  0.5012,    Adjusted R-squared:  0.4901 
F-statistic: 45.21 on 2 and 90 DF,  p-value: 2.56e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2916 -0.8067 -0.0294  0.6042  3.4958 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.225e+00  3.902e-01  -3.139   0.0023 **
femininity   7.171e-02  2.534e-01   0.283   0.7779   
damage       3.012e-01  1.535e+00   0.196   0.8448   
damage:year  6.883e-05  7.721e-04   0.089   0.9292   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.089 on 89 degrees of freedom
Multiple R-squared:  0.5012,    Adjusted R-squared:  0.4844 
F-statistic: 29.81 on 3 and 89 DF,  p-value: 1.961e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3204 -0.8090 -0.0225  0.6009  3.4855 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.217740   0.391316  -3.112   0.0025 ** 
femininity   0.052201   0.256478   0.204   0.8392    
damage       0.440998   0.050440   8.743 1.28e-13 ***
damage:post -0.004747   0.030288  -0.157   0.8758    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.089 on 89 degrees of freedom
Multiple R-squared:  0.5013,    Adjusted R-squared:  0.4845 
F-statistic: 29.82 on 3 and 89 DF,  p-value: 1.945e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3153 -0.7909  0.0279  0.5917  3.4428 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.63652    0.56663  -1.123    0.264    
femininity        -0.92229    0.73901  -1.248    0.215    
damage             0.35627    0.07403   4.813 6.05e-06 ***
femininity:damage  0.13383    0.09472   1.413    0.161    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.077 on 89 degrees of freedom
Multiple R-squared:  0.5121,    Adjusted R-squared:  0.4957 
F-statistic: 31.14 on 3 and 89 DF,  p-value: 7.413e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2785 -0.7481  0.0136  0.6084  3.4791 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.6284221  0.5704660  -1.102    0.274
femininity        -0.9254243  0.7430050  -1.246    0.216
damage            -0.0458785  1.5452101  -0.030    0.976
femininity:damage  0.1368363  0.0959196   1.427    0.157
damage:year        0.0002015  0.0007732   0.261    0.795

Residual standard error: 1.082 on 88 degrees of freedom
Multiple R-squared:  0.5125,    Adjusted R-squared:  0.4903 
F-statistic: 23.13 on 4 and 88 DF,  p-value: 4.402e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3151 -0.7906  0.0276  0.5919  3.4430 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.6364922  0.5699690  -1.117    0.267    
femininity        -0.9222850  0.7431990  -1.241    0.218    
damage             0.3562133  0.0787766   4.522  1.9e-05 ***
femininity:damage  0.1338539  0.0958815   1.396    0.166    
damage:post        0.0000635  0.0303243   0.002    0.998    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.083 on 88 degrees of freedom
Multiple R-squared:  0.5121,    Adjusted R-squared:  0.4899 
F-statistic: 23.09 on 4 and 88 DF,  p-value: 4.551e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7450 -0.8504 -0.2053  0.8609  4.1735 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.430e+00  2.980e-01   4.799 6.39e-06 ***
femininity  2.255e-02  4.216e-02   0.535    0.594    
damage      4.963e-05  6.026e-06   8.236 1.42e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.142 on 89 degrees of freedom
Multiple R-squared:  0.4406,    Adjusted R-squared:  0.428 
F-statistic: 35.05 on 2 and 89 DF,  p-value: 5.942e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2883 -0.8261 -0.1901  0.8378  4.1641 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.456e+00  2.984e-01   4.881 4.67e-06 ***
femininity   1.481e-02  4.262e-02   0.348    0.729    
damage       6.406e-04  5.143e-04   1.245    0.216    
damage:year -2.966e-07  2.582e-07  -1.149    0.254    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.14 on 88 degrees of freedom
Multiple R-squared:  0.4489,    Adjusted R-squared:  0.4301 
F-statistic: 23.89 on 3 and 88 DF,  p-value: 2.115e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3891 -0.8205 -0.1882  0.8765  4.1648 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.463e+00  2.980e-01   4.909 4.17e-06 ***
femininity   1.371e-02  4.255e-02   0.322    0.748    
damage       6.052e-05  1.034e-05   5.850 8.23e-08 ***
damage:post -1.500e-05  1.160e-05  -1.293    0.199    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 88 degrees of freedom
Multiple R-squared:  0.451, Adjusted R-squared:  0.4323 
F-statistic:  24.1 on 3 and 88 DF,  p-value: 1.783e-11
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7498 -0.7723 -0.2310  0.8905  4.1586 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.199e+00  3.503e-01   3.424 0.000939 ***
femininity         5.163e-02  4.810e-02   1.073 0.286024    
damage             7.744e-05  2.316e-05   3.343 0.001217 ** 
femininity:damage -3.344e-06  2.689e-06  -1.243 0.217020    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.138 on 88 degrees of freedom
Multiple R-squared:  0.4502,    Adjusted R-squared:  0.4315 
F-statistic: 24.02 on 3 and 88 DF,  p-value: 1.895e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2063 -0.8045 -0.2392  0.8295  4.1479 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.215e+00  3.496e-01   3.476 0.000795 ***
femininity         4.494e-02  4.828e-02   0.931 0.354488    
damage             7.054e-04  5.147e-04   1.371 0.174014    
femininity:damage -3.518e-06  2.686e-06  -1.310 0.193707    
damage:year       -3.145e-07  2.575e-07  -1.221 0.225235    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.135 on 87 degrees of freedom
Multiple R-squared:  0.4595,    Adjusted R-squared:  0.4347 
F-statistic: 18.49 on 4 and 87 DF,  p-value: 4.989e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3100 -0.8031 -0.2295  0.8468  4.1485 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.220e+00  3.489e-01   3.496 0.000745 ***
femininity         4.410e-02  4.818e-02   0.915 0.362527    
damage             9.070e-05  2.499e-05   3.629 0.000479 ***
femininity:damage -3.552e-06  2.680e-06  -1.325 0.188554    
damage:post       -1.587e-05  1.157e-05  -1.371 0.173755    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.133 on 87 degrees of freedom
Multiple R-squared:  0.4619,    Adjusted R-squared:  0.4371 
F-statistic: 18.67 on 4 and 87 DF,  p-value: 4.142e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3194 -0.8334 -0.0169  0.6006  3.4540 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.32828    0.43271  -3.070  0.00284 ** 
femininity   0.02736    0.04001   0.684  0.49585    
damage       0.43376    0.04723   9.185 1.56e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 89 degrees of freedom
Multiple R-squared:  0.4939,    Adjusted R-squared:  0.4826 
F-statistic: 43.43 on 2 and 89 DF,  p-value: 6.869e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2973 -0.8410 -0.0310  0.6167  3.4753 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.3340423  0.4368096  -3.054  0.00299 **
femininity   0.0288081  0.0413783   0.696  0.48813   
damage       0.2066194  1.5189397   0.136  0.89211   
damage:year  0.0001144  0.0007645   0.150  0.88141   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 88 degrees of freedom
Multiple R-squared:  0.4941,    Adjusted R-squared:  0.4768 
F-statistic: 28.65 on 3 and 88 DF,  p-value: 5.123e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3326 -0.8316 -0.0152  0.5926  3.4414 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.321884   0.438465  -3.015  0.00336 ** 
femininity   0.026185   0.041445   0.632  0.52916    
damage       0.435811   0.050536   8.624 2.44e-13 ***
damage:post -0.003543   0.029890  -0.119  0.90592    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 88 degrees of freedom
Multiple R-squared:  0.494, Adjusted R-squared:  0.4768 
F-statistic: 28.64 on 3 and 88 DF,  p-value: 5.144e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3234 -0.7464  0.0091  0.6018  3.4072 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)  
(Intercept)       -0.09655    0.86778  -0.111   0.9117  
femininity        -0.17239    0.12862  -1.340   0.1836  
damage             0.26555    0.11316   2.347   0.0212 *
femininity:damage  0.02688    0.01647   1.633   0.1061  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.076 on 88 degrees of freedom
Multiple R-squared:  0.5088,    Adjusted R-squared:  0.4921 
F-statistic: 30.39 on 3 and 88 DF,  p-value: 1.414e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2749 -0.7715  0.0103  0.6110  3.4529 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.0818621  0.8733438  -0.094    0.926
femininity        -0.1736525  0.1293318  -1.343    0.183
damage            -0.2366707  1.5280647  -0.155    0.877
femininity:damage  0.0274784  0.0166488   1.650    0.102
damage:year        0.0002510  0.0007616   0.330    0.743

Residual standard error: 1.081 on 87 degrees of freedom
Multiple R-squared:  0.5094,    Adjusted R-squared:  0.4869 
F-statistic: 22.59 on 4 and 87 DF,  p-value: 8.132e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3205 -0.7472  0.0079  0.6047  3.4101 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)       -0.0961687  0.8728692  -0.110   0.9125  
femininity        -0.1724236  0.1293610  -1.333   0.1860  
damage             0.2648647  0.1168569   2.267   0.0259 *
femininity:damage  0.0269207  0.0166274   1.619   0.1091  
damage:post        0.0007734  0.0297385   0.026   0.9793  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.082 on 87 degrees of freedom
Multiple R-squared:  0.5088,    Adjusted R-squared:  0.4862 
F-statistic: 22.53 on 4 and 87 DF,  p-value: 8.572e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7673 -0.8029 -0.2246  0.8378  4.1646 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.453e+00  2.170e-01   6.699 1.84e-09 ***
femininity  1.793e-01  2.569e-01   0.698    0.487    
damage      4.963e-05  6.001e-06   8.270 1.21e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.14 on 89 degrees of freedom
Multiple R-squared:  0.4418,    Adjusted R-squared:  0.4293 
F-statistic: 35.23 on 2 and 89 DF,  p-value: 5.376e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2736 -0.7824 -0.2112  0.8175  4.1550 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.464e+00  2.169e-01   6.752  1.5e-09 ***
femininity   1.297e-01  2.604e-01   0.498    0.620    
damage       6.250e-04  5.150e-04   1.214    0.228    
damage:year -2.888e-07  2.585e-07  -1.117    0.267    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 88 degrees of freedom
Multiple R-squared:  0.4497,    Adjusted R-squared:  0.4309 
F-statistic: 23.97 on 3 and 88 DF,  p-value: 1.986e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3734 -0.7828 -0.2077  0.8603  4.1561 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.470e+00  2.166e-01   6.784 1.30e-09 ***
femininity   1.212e-01  2.602e-01   0.466    0.642    
damage       6.024e-05  1.033e-05   5.830 8.98e-08 ***
damage:post -1.464e-05  1.163e-05  -1.259    0.211    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 88 degrees of freedom
Multiple R-squared:  0.4517,    Adjusted R-squared:  0.433 
F-statistic: 24.17 on 3 and 88 DF,  p-value: 1.686e-11
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7828 -0.7517 -0.2697  0.8629  4.1526 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.357e+00  2.514e-01   5.396  5.7e-07 ***
femininity         2.964e-01  2.993e-01   0.990 0.324646    
damage             6.193e-05  1.711e-05   3.620 0.000491 ***
femininity:damage -1.404e-05  1.827e-05  -0.768 0.444465    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.143 on 88 degrees of freedom
Multiple R-squared:  0.4456,    Adjusted R-squared:  0.4267 
F-statistic: 23.57 on 3 and 88 DF,  p-value: 2.739e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2329 -0.7535 -0.2343  0.8173  4.1379 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.340e+00  2.509e-01   5.343 7.23e-07 ***
femininity         2.738e-01  2.987e-01   0.917    0.362    
damage             7.323e-04  5.265e-04   1.391    0.168    
femininity:damage -1.822e-05  1.850e-05  -0.984    0.328    
damage:year       -3.347e-07  2.627e-07  -1.274    0.206    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 87 degrees of freedom
Multiple R-squared:  0.4557,    Adjusted R-squared:  0.4307 
F-statistic: 18.21 on 4 and 87 DF,  p-value: 6.713e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3358 -0.7831 -0.2310  0.8530  4.1388 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.342e+00  2.502e-01   5.366 6.59e-07 ***
femininity         2.694e-01  2.981e-01   0.904 0.368728    
damage             7.828e-05  2.053e-05   3.813 0.000256 ***
femininity:damage -1.879e-05  1.847e-05  -1.017 0.311908    
damage:post       -1.682e-05  1.182e-05  -1.423 0.158448    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.136 on 87 degrees of freedom
Multiple R-squared:  0.4582,    Adjusted R-squared:  0.4333 
F-statistic: 18.39 on 4 and 87 DF,  p-value: 5.545e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3036 -0.8262 -0.0320  0.6307  3.4854 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.21441    0.39031  -3.111   0.0025 ** 
femininity   0.07995    0.24616   0.325   0.7461    
damage       0.43493    0.04748   9.160 1.75e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.088 on 89 degrees of freedom
Multiple R-squared:  0.4919,    Adjusted R-squared:  0.4805 
F-statistic: 43.08 on 2 and 89 DF,  p-value: 8.228e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2921 -0.8282 -0.0288  0.6199  3.4970 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.216e+00  3.930e-01  -3.094  0.00265 **
femininity   8.578e-02  2.579e-01   0.333  0.74024   
damage       3.107e-01  1.543e+00   0.201  0.84084   
damage:year  6.253e-05  7.761e-04   0.081  0.93597   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.094 on 88 degrees of freedom
Multiple R-squared:  0.4919,    Adjusted R-squared:  0.4746 
F-statistic:  28.4 on 3 and 88 DF,  p-value: 6.16e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3231 -0.8256 -0.0370  0.6181  3.5049 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.207270   0.394289  -3.062  0.00292 ** 
femininity   0.064963   0.260114   0.250  0.80336    
damage       0.438394   0.051190   8.564 3.24e-13 ***
damage:post -0.005723   0.030555  -0.187  0.85184    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.094 on 88 degrees of freedom
Multiple R-squared:  0.4921,    Adjusted R-squared:  0.4748 
F-statistic: 28.42 on 3 and 88 DF,  p-value: 6.074e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3153 -0.8093  0.0123  0.5643  3.4428 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54721    0.58221  -0.940    0.350    
femininity        -1.01160    0.75187  -1.345    0.182    
damage             0.34007    0.07772   4.376 3.32e-05 ***
femininity:damage  0.15003    0.09774   1.535    0.128    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.08 on 88 degrees of freedom
Multiple R-squared:  0.5051,    Adjusted R-squared:  0.4883 
F-statistic: 29.94 on 3 and 88 DF,  p-value: 1.957e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2780 -0.7580  0.0127  0.5699  3.4796 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.5388357  0.5861718  -0.919    0.361
femininity        -1.0149430  0.7559835  -1.343    0.183
damage            -0.0675949  1.5499894  -0.044    0.965
femininity:damage  0.1531100  0.0989500   1.547    0.125
damage:year        0.0002042  0.0007755   0.263    0.793

Residual standard error: 1.086 on 87 degrees of freedom
Multiple R-squared:  0.5055,    Adjusted R-squared:  0.4828 
F-statistic: 22.24 on 4 and 87 DF,  p-value: 1.139e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3196 -0.8134  0.0142  0.5673  3.4381 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.547387   0.585550  -0.935    0.352    
femininity        -1.012054   0.756247  -1.338    0.184    
damage             0.341098   0.081888   4.165  7.3e-05 ***
femininity:damage  0.149632   0.098752   1.515    0.133    
damage:post       -0.001289   0.030473  -0.042    0.966    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 87 degrees of freedom
Multiple R-squared:  0.5051,    Adjusted R-squared:  0.4824 
F-statistic:  22.2 on 4 and 87 DF,  p-value: 1.177e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9798 -0.7962 -0.1807  0.8581  4.1661 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.394e+00  2.969e-01   4.696 9.69e-06 ***
femininity  2.571e-02  4.192e-02   0.613    0.541    
damage      5.300e-05  6.389e-06   8.295 1.16e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.134 on 88 degrees of freedom
Multiple R-squared:  0.4455,    Adjusted R-squared:  0.4329 
F-statistic: 35.35 on 2 and 88 DF,  p-value: 5.39e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7268 -0.8023 -0.1235  0.7492  4.1323 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.422e+00  2.880e-01   4.935 3.82e-06 ***
femininity   8.925e-03  4.116e-02   0.217  0.82883    
damage       1.626e-03  6.112e-04   2.659  0.00932 ** 
damage:year -7.873e-07  3.060e-07  -2.573  0.01178 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.099 on 87 degrees of freedom
Multiple R-squared:  0.4847,    Adjusted R-squared:  0.4669 
F-statistic: 27.28 on 3 and 87 DF,  p-value: 1.576e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7203 -0.8041 -0.1169  0.7847  4.1338 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.432e+00  2.862e-01   5.004 2.89e-06 ***
femininity   6.991e-03  4.090e-02   0.171  0.86468    
damage       8.570e-05  1.314e-05   6.523 4.38e-09 ***
damage:post -3.890e-05  1.381e-05  -2.817  0.00599 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.091 on 87 degrees of freedom
Multiple R-squared:  0.4919,    Adjusted R-squared:  0.4743 
F-statistic: 28.07 on 3 and 87 DF,  p-value: 8.642e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7481 -0.7231 -0.1892  0.9074  4.1532 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.189e+00  3.485e-01   3.413 0.000977 ***
femininity         5.163e-02  4.784e-02   1.079 0.283457    
damage             7.776e-05  2.304e-05   3.375 0.001103 ** 
femininity:damage -3.005e-06  2.686e-06  -1.119 0.266305    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.132 on 87 degrees of freedom
Multiple R-squared:  0.4534,    Adjusted R-squared:  0.4345 
F-statistic: 24.05 on 3 and 87 DF,  p-value: 1.992e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7313 -0.7997 -0.1737  0.7495  4.1194 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.216e+00  3.379e-01   3.600 0.000531 ***
femininity         3.488e-02  4.681e-02   0.745 0.458234    
damage             1.651e-03  6.104e-04   2.704 0.008250 ** 
femininity:damage -3.009e-06  2.603e-06  -1.156 0.250812    
damage:year       -7.875e-07  3.054e-07  -2.579 0.011621 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.097 on 86 degrees of freedom
Multiple R-squared:  0.4926,    Adjusted R-squared:  0.469 
F-statistic: 20.87 on 4 and 86 DF,  p-value: 4.744e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7247 -0.8088 -0.1457  0.7755  4.1208 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.225e+00  3.355e-01   3.652 0.000446 ***
femininity         3.314e-02  4.649e-02   0.713 0.477881    
damage             1.108e-04  2.505e-05   4.422 2.84e-05 ***
femininity:damage -3.035e-06  2.584e-06  -1.174 0.243486    
damage:post       -3.897e-05  1.378e-05  -2.828 0.005825 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.089 on 86 degrees of freedom
Multiple R-squared:  0.4999,    Adjusted R-squared:  0.4766 
F-statistic: 21.49 on 4 and 86 DF,  p-value: 2.584e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3170 -0.8365 -0.0298  0.6056  3.4572 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.32054    0.43978  -3.003  0.00348 ** 
femininity   0.02700    0.04035   0.669  0.50507    
damage       0.43284    0.04809   9.001 4.09e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 88 degrees of freedom
Multiple R-squared:  0.4856,    Adjusted R-squared:  0.4739 
F-statistic: 41.53 on 2 and 88 DF,  p-value: 1.988e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2906 -0.8514 -0.0514  0.6259  3.4827 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.3254192  0.4431448  -2.991  0.00362 **
femininity   0.0286012  0.0416340   0.687  0.49393   
damage       0.1679877  1.5498234   0.108  0.91393   
damage:year  0.0001333  0.0007794   0.171  0.86464   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.098 on 87 degrees of freedom
Multiple R-squared:  0.4857,    Adjusted R-squared:  0.468 
F-statistic: 27.39 on 3 and 87 DF,  p-value: 1.445e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3287 -0.8336 -0.0199  0.6057  3.4450 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.315982   0.444571  -2.960  0.00396 ** 
femininity   0.026029   0.041707   0.624  0.53421    
damage       0.434733   0.051864   8.382 8.25e-13 ***
damage:post -0.003067   0.030405  -0.101  0.91990    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.098 on 87 degrees of freedom
Multiple R-squared:  0.4856,    Adjusted R-squared:  0.4679 
F-statistic: 27.38 on 3 and 87 DF,  p-value: 1.459e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3257 -0.7506  0.0149  0.6030  3.4037 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)  
(Intercept)       -0.09089    0.87403  -0.104   0.9174  
femininity        -0.17417    0.13024  -1.337   0.1846  
damage             0.26465    0.11406   2.320   0.0227 *
femininity:damage  0.02717    0.01674   1.623   0.1082  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.082 on 87 degrees of freedom
Multiple R-squared:  0.5007,    Adjusted R-squared:  0.4835 
F-statistic: 29.08 on 3 and 87 DF,  p-value: 4.066e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2778 -0.7750  0.0205  0.6138  3.4494 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.0790043  0.8794106  -0.090    0.929
femininity        -0.1746565  0.1309318  -1.334    0.186
damage            -0.2214437  1.5534672  -0.143    0.887
femininity:damage  0.0276275  0.0168909   1.636    0.106
damage:year        0.0002431  0.0007749   0.314    0.754

Residual standard error: 1.088 on 86 degrees of freedom
Multiple R-squared:  0.5013,    Adjusted R-squared:  0.4781 
F-statistic: 21.61 on 4 and 86 DF,  p-value: 2.301e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3246 -0.7502  0.0136  0.6045  3.4049 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)       -0.0908122  0.8791326  -0.103   0.9180  
femininity        -0.1741624  0.1310009  -1.329   0.1872  
damage             0.2643951  0.1175985   2.248   0.0271 *
femininity:damage  0.0271795  0.0168773   1.610   0.1110  
damage:post        0.0002964  0.0302027   0.010   0.9922  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.088 on 86 degrees of freedom
Multiple R-squared:  0.5007,    Adjusted R-squared:  0.4775 
F-statistic: 21.56 on 4 and 86 DF,  p-value: 2.414e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9688 -0.7413 -0.2001  0.8418  4.1588 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.427e+00  2.162e-01   6.600 2.98e-09 ***
femininity  1.957e-01  2.553e-01   0.766    0.445    
damage      5.303e-05  6.369e-06   8.325 1.00e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.132 on 88 degrees of freedom
Multiple R-squared:  0.4468,    Adjusted R-squared:  0.4343 
F-statistic: 35.54 on 2 and 88 DF,  p-value: 4.853e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7388 -0.8070 -0.1288  0.7583  4.1262 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.425e+00  2.098e-01   6.793  1.3e-09 ***
femininity   8.090e-02  2.518e-01   0.321   0.7488    
damage       1.611e-03  6.132e-04   2.627   0.0102 *  
damage:year -7.802e-07  3.070e-07  -2.541   0.0128 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.099 on 87 degrees of freedom
Multiple R-squared:  0.485, Adjusted R-squared:  0.4673 
F-statistic: 27.32 on 3 and 87 DF,  p-value: 1.533e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7299 -0.8082 -0.1223  0.7974  4.1289 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.434e+00  2.083e-01   6.884 8.57e-10 ***
femininity   6.401e-02  2.506e-01   0.255  0.79895    
damage       8.546e-05  1.317e-05   6.490 5.08e-09 ***
damage:post -3.862e-05  1.387e-05  -2.784  0.00659 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.091 on 87 degrees of freedom
Multiple R-squared:  0.4921,    Adjusted R-squared:  0.4746 
F-statistic: 28.09 on 3 and 87 DF,  p-value: 8.49e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7776 -0.7076 -0.2520  0.8806  4.1503 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.357e+00  2.500e-01   5.426 5.13e-07 ***
femininity         2.813e-01  2.978e-01   0.945 0.347418    
damage             6.193e-05  1.701e-05   3.640 0.000462 ***
femininity:damage -1.037e-05  1.836e-05  -0.565 0.573542    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 87 degrees of freedom
Multiple R-squared:  0.4488,    Adjusted R-squared:  0.4298 
F-statistic: 23.62 on 3 and 87 DF,  p-value: 2.836e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7554 -0.8255 -0.1612  0.7483  4.1118 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.317e+00  2.424e-01   5.434 5.07e-07 ***
femininity         2.074e-01  2.896e-01   0.716  0.47576    
damage             1.689e-03  6.203e-04   2.724  0.00782 ** 
femininity:damage -1.590e-05  1.789e-05  -0.888  0.37680    
damage:year       -8.125e-07  3.095e-07  -2.625  0.01026 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.1 on 86 degrees of freedom
Multiple R-squared:  0.4897,    Adjusted R-squared:  0.466 
F-statistic: 20.63 on 4 and 86 DF,  p-value: 6.013e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7468 -0.8278 -0.1457  0.7580  4.1140 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.323e+00  2.405e-01   5.499 3.85e-07 ***
femininity         1.955e-01  2.877e-01   0.680   0.4986    
damage             1.010e-04  2.126e-05   4.751 8.03e-06 ***
femininity:damage -1.657e-05  1.777e-05  -0.933   0.3537    
damage:post       -4.020e-05  1.399e-05  -2.874   0.0051 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 86 degrees of freedom
Multiple R-squared:  0.4972,    Adjusted R-squared:  0.4738 
F-statistic: 21.26 on 4 and 86 DF,  p-value: 3.247e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3006 -0.8387 -0.0525  0.6362  3.4891 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.20615    0.39609  -3.045  0.00307 ** 
femininity   0.07798    0.24785   0.315  0.75378    
damage       0.43376    0.04835   8.972  4.7e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.094 on 88 degrees of freedom
Multiple R-squared:  0.4835,    Adjusted R-squared:  0.4718 
F-statistic: 41.19 on 2 and 88 DF,  p-value: 2.365e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2846 -0.8448 -0.0477  0.6320  3.5052 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.207e+00  3.985e-01  -3.030  0.00322 **
femininity   8.570e-02  2.593e-01   0.330  0.74186   
damage       2.642e-01  1.575e+00   0.168  0.86719   
damage:year  8.527e-05  7.919e-04   0.108  0.91450   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.1 on 87 degrees of freedom
Multiple R-squared:  0.4836,    Adjusted R-squared:  0.4658 
F-statistic: 27.16 on 3 and 87 DF,  p-value: 1.728e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3185 -0.8308 -0.0723  0.6367  3.5076 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.201158   0.399457  -3.007  0.00345 ** 
femininity   0.064924   0.261581   0.248  0.80457    
damage       0.437049   0.052570   8.314 1.14e-12 ***
damage:post -0.005115   0.031103  -0.164  0.86976    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.1 on 87 degrees of freedom
Multiple R-squared:  0.4837,    Adjusted R-squared:  0.4659 
F-statistic: 27.17 on 3 and 87 DF,  p-value: 1.715e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3159 -0.8283  0.0266  0.5693  3.4420 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54721    0.58554  -0.935    0.353    
femininity        -1.01384    0.75986  -1.334    0.186    
damage             0.34007    0.07816   4.351 3.68e-05 ***
femininity:damage  0.15039    0.09903   1.519    0.132    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 87 degrees of freedom
Multiple R-squared:  0.4969,    Adjusted R-squared:  0.4795 
F-statistic: 28.64 on 3 and 87 DF,  p-value: 5.642e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2775 -0.7677  0.0132  0.5730  3.4801 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.5387773  0.5895915  -0.914    0.363
femininity        -1.0140929  0.7639679  -1.327    0.188
damage            -0.0704391  1.5785677  -0.045    0.965
femininity:damage  0.1529910  0.1000626   1.529    0.130
damage:year        0.0002056  0.0007898   0.260    0.795

Residual standard error: 1.092 on 86 degrees of freedom
Multiple R-squared:  0.4973,    Adjusted R-squared:  0.4739 
F-statistic: 21.27 on 4 and 86 DF,  p-value: 3.216e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3209 -0.8318  0.0343  0.5719  3.4365 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.547409   0.588941  -0.929    0.355    
femininity        -1.014873   0.764575  -1.327    0.188    
damage             0.341229   0.082441   4.139  8.1e-05 ***
femininity:damage  0.150025   0.099912   1.502    0.137    
damage:post       -0.001453   0.030978  -0.047    0.963    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 86 degrees of freedom
Multiple R-squared:  0.4969,    Adjusted R-squared:  0.4735 
F-statistic: 21.23 on 4 and 86 DF,  p-value: 3.321e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7964 -0.7723 -0.1779  0.8740  4.1528 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.308e+00  3.006e-01   4.352 3.56e-05 ***
femininity  3.864e-02  4.142e-02   0.933    0.353    
damage      5.055e-05  6.743e-06   7.498 4.37e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.141 on 90 degrees of freedom
Multiple R-squared:  0.3872,    Adjusted R-squared:  0.3736 
F-statistic: 28.43 on 2 and 90 DF,  p-value: 2.686e-10
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0505 -0.7823 -0.1833  0.8494  4.1430 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.342e+00  3.055e-01   4.394 3.07e-05 ***
femininity   3.270e-02  4.242e-02   0.771    0.443    
damage       4.297e-04  5.484e-04   0.783    0.435    
damage:year -1.909e-07  2.761e-07  -0.691    0.491    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.144 on 89 degrees of freedom
Multiple R-squared:  0.3905,    Adjusted R-squared:  0.3699 
F-statistic: 19.01 on 3 and 89 DF,  p-value: 1.304e-09
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3833 -0.7727 -0.1690  0.8780  4.1450 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.382e+00  3.059e-01   4.519 1.91e-05 ***
femininity   2.561e-02  4.268e-02   0.600    0.550    
damage       6.013e-05  1.034e-05   5.814 9.40e-08 ***
damage:post -1.498e-05  1.230e-05  -1.218    0.226    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.138 on 89 degrees of freedom
Multiple R-squared:  0.3973,    Adjusted R-squared:  0.3769 
F-statistic: 19.55 on 3 and 89 DF,  p-value: 7.995e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1236 -0.8262 -0.1684  0.8658  4.1880 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.463e+00  3.337e-01   4.386 3.16e-05 ***
femininity        1.317e-02  4.777e-02   0.276   0.7834    
damage            3.672e-05  1.460e-05   2.514   0.0137 *  
femininity:damage 2.306e-06  2.159e-06   1.068   0.2883    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.14 on 89 degrees of freedom
Multiple R-squared:  0.395, Adjusted R-squared:  0.3746 
F-statistic: 19.37 on 3 and 89 DF,  p-value: 9.439e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2532 -0.8159 -0.1981  0.8547  4.1791 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.461e+00  3.354e-01   4.357 3.56e-05 ***
femininity         1.332e-02  4.801e-02   0.277    0.782    
damage             2.237e-04  5.984e-04   0.374    0.709    
femininity:damage  2.030e-06  2.343e-06   0.866    0.389    
damage:year       -9.331e-08  2.985e-07  -0.313    0.755    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.146 on 88 degrees of freedom
Multiple R-squared:  0.3956,    Adjusted R-squared:  0.3682 
F-statistic:  14.4 on 4 and 88 DF,  p-value: 4.387e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4315 -0.8050 -0.2037  0.8362  4.1653 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.444e+00  3.356e-01   4.302 4.37e-05 ***
femininity         1.577e-02  4.802e-02   0.328   0.7433    
damage             5.044e-05  2.371e-05   2.127   0.0362 *  
femininity:damage  1.198e-06  2.637e-06   0.454   0.6507    
damage:post       -1.108e-05  1.505e-05  -0.736   0.4638    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.143 on 88 degrees of freedom
Multiple R-squared:  0.3987,    Adjusted R-squared:  0.3713 
F-statistic: 14.59 on 4 and 88 DF,  p-value: 3.542e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2748 -0.8361 -0.0155  0.6810  3.5146 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.15195    0.41217  -2.795  0.00635 ** 
femininity   0.01396    0.03733   0.374  0.70931    
damage       0.41938    0.04408   9.514 2.93e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.027 on 90 degrees of freedom
Multiple R-squared:  0.5036,    Adjusted R-squared:  0.4926 
F-statistic: 45.66 on 2 and 90 DF,  p-value: 2.048e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3048 -0.8316  0.0094  0.6537  3.4858 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.1436217  0.4161594  -2.748  0.00726 **
femininity   0.0118812  0.0387457   0.307  0.75983   
damage       0.7274208  1.4248388   0.511  0.61095   
damage:year -0.0001551  0.0007170  -0.216  0.82925   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.032 on 89 degrees of freedom
Multiple R-squared:  0.5039,    Adjusted R-squared:  0.4872 
F-statistic: 30.13 on 3 and 89 DF,  p-value: 1.547e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3240 -0.7952  0.0116  0.6206  3.4640 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.125916   0.417627  -2.696  0.00839 ** 
femininity   0.009045   0.038917   0.232  0.81674    
damage       0.427279   0.047334   9.027  3.3e-14 ***
damage:post -0.013304   0.028196  -0.472  0.63821    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.031 on 89 degrees of freedom
Multiple R-squared:  0.5049,    Adjusted R-squared:  0.4882 
F-statistic: 30.25 on 3 and 89 DF,  p-value: 1.418e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2805 -0.7718  0.0576  0.6035  3.4844 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.48657    0.80879  -0.602  0.54897   
femininity        -0.09696    0.12185  -0.796  0.42832   
damage             0.33003    0.10332   3.194  0.00194 **
femininity:damage  0.01477    0.01544   0.956  0.34150   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.027 on 89 degrees of freedom
Multiple R-squared:  0.5087,    Adjusted R-squared:  0.4921 
F-statistic: 30.71 on 3 and 89 DF,  p-value: 1.009e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2929 -0.7626  0.0605  0.6151  3.4728 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -4.916e-01  8.153e-01  -0.603    0.548
femininity        -9.641e-02  1.227e-01  -0.786    0.434
damage             4.592e-01  1.455e+00   0.316    0.753
femininity:damage  1.458e-02  1.567e-02   0.930    0.355
damage:year       -6.445e-05  7.241e-04  -0.089    0.929

Residual standard error: 1.033 on 88 degrees of freedom
Multiple R-squared:  0.5087,    Adjusted R-squared:  0.4864 
F-statistic: 22.78 on 4 and 88 DF,  p-value: 6.13e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3166 -0.7684  0.0322  0.5895  3.4484 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.500455   0.813817  -0.615  0.54018   
femininity        -0.095069   0.122581  -0.776  0.44008   
damage             0.340315   0.108028   3.150  0.00223 **
femininity:damage  0.014034   0.015667   0.896  0.37280   
damage:post       -0.009832   0.028492  -0.345  0.73085   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.032 on 88 degrees of freedom
Multiple R-squared:  0.5093,    Adjusted R-squared:  0.487 
F-statistic: 22.84 on 4 and 88 DF,  p-value: 5.805e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7958 -0.7020 -0.2530  0.8701  4.1544 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.385e+00  2.200e-01   6.294 1.10e-08 ***
femininity  2.540e-01  2.528e-01   1.004    0.318    
damage      5.046e-05  6.737e-06   7.490 4.52e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.14 on 90 degrees of freedom
Multiple R-squared:  0.3881,    Adjusted R-squared:  0.3745 
F-statistic: 28.55 on 2 and 90 DF,  p-value: 2.508e-10
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0341 -0.7205 -0.2083  0.8500  4.1427 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.404e+00  2.223e-01   6.314 1.04e-08 ***
femininity   2.206e-01  2.581e-01   0.855    0.395    
damage       4.280e-04  5.463e-04   0.784    0.435    
damage:year -1.901e-07  2.750e-07  -0.691    0.491    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.143 on 89 degrees of freedom
Multiple R-squared:  0.3914,    Adjusted R-squared:  0.3709 
F-statistic: 19.08 on 3 and 89 DF,  p-value: 1.22e-09
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3655 -0.7436 -0.2193  0.8615  4.1430 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.426e+00  2.221e-01   6.421 6.44e-09 ***
femininity   1.790e-01  2.597e-01   0.689    0.492    
damage       5.996e-05  1.034e-05   5.800 9.96e-08 ***
damage:post -1.482e-05  1.225e-05  -1.209    0.230    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 89 degrees of freedom
Multiple R-squared:  0.398, Adjusted R-squared:  0.3777 
F-statistic: 19.62 on 3 and 89 DF,  p-value: 7.557e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0279 -0.7934 -0.2013  0.8308  4.1864 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.478e+00  2.378e-01   6.216 1.61e-08 ***
femininity        1.027e-01  2.922e-01   0.351 0.726177    
damage            4.169e-05  1.085e-05   3.841 0.000229 ***
femininity:damage 1.427e-05  1.384e-05   1.031 0.305377    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 89 degrees of freedom
Multiple R-squared:  0.3954,    Adjusted R-squared:  0.375 
F-statistic:  19.4 on 3 and 89 DF,  p-value: 9.171e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2042 -0.7882 -0.2000  0.8238  4.1755 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.477e+00  2.390e-01   6.183 1.93e-08 ***
femininity         1.020e-01  2.937e-01   0.347    0.729    
damage             2.653e-04  5.795e-04   0.458    0.648    
femininity:damage  1.248e-05  1.466e-05   0.852    0.397    
damage:year       -1.120e-07  2.903e-07  -0.386    0.700    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 88 degrees of freedom
Multiple R-squared:  0.3964,    Adjusted R-squared:  0.3689 
F-statistic: 14.45 on 4 and 88 DF,  p-value: 4.162e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3985 -0.7786 -0.2019  0.8422  4.1631 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.467e+00  2.387e-01   6.145 2.28e-08 ***
femininity         1.148e-01  2.933e-01   0.391  0.69651    
damage             5.294e-05  1.796e-05   2.948  0.00409 ** 
femininity:damage  7.738e-06  1.616e-05   0.479  0.63319    
damage:post       -1.129e-05  1.434e-05  -0.788  0.43307    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.142 on 88 degrees of freedom
Multiple R-squared:  0.3996,    Adjusted R-squared:  0.3723 
F-statistic: 14.64 on 4 and 88 DF,  p-value: 3.316e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2657 -0.8278 -0.0038  0.6977  3.5320 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.09066    0.36890  -2.956  0.00397 ** 
femininity   0.04009    0.22894   0.175  0.86139    
damage       0.41953    0.04425   9.480 3.44e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.027 on 90 degrees of freedom
Multiple R-squared:  0.503, Adjusted R-squared:  0.492 
F-statistic: 45.55 on 2 and 90 DF,  p-value: 2.163e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3009 -0.8008 -0.0119  0.6641  3.4965 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.0860267  0.3712406  -2.925  0.00436 **
femininity   0.0222591  0.2398941   0.093  0.92628   
damage       0.7984476  1.4401062   0.554  0.58067   
damage:year -0.0001907  0.0007244  -0.263  0.79297   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.033 on 89 degrees of freedom
Multiple R-squared:  0.5034,    Adjusted R-squared:  0.4867 
F-statistic: 30.07 on 3 and 89 DF,  p-value: 1.614e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3172 -0.7629  0.0206  0.6296  3.4752 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.072016   0.372084  -2.881  0.00497 ** 
femininity  -0.001299   0.242962  -0.005  0.99575    
damage       0.428918   0.047883   8.958  4.6e-14 ***
damage:post -0.015108   0.028723  -0.526  0.60021    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.031 on 89 degrees of freedom
Multiple R-squared:  0.5046,    Adjusted R-squared:  0.4879 
F-statistic: 30.21 on 3 and 89 DF,  p-value: 1.456e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2748 -0.7232  0.0602  0.6131  3.4999 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.63652    0.53954  -1.180    0.241    
femininity        -0.72833    0.70529  -1.033    0.305    
damage             0.35627    0.07049   5.054 2.29e-06 ***
femininity:damage  0.10417    0.09045   1.152    0.253    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.025 on 89 degrees of freedom
Multiple R-squared:  0.5103,    Adjusted R-squared:  0.4938 
F-statistic: 30.92 on 3 and 89 DF,  p-value: 8.705e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2921 -0.7369  0.0506  0.6082  3.4828 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -6.403e-01  5.433e-01  -1.179    0.242
femininity        -7.269e-01  7.093e-01  -1.025    0.308
damage             5.451e-01  1.456e+00   0.374    0.709
femininity:damage  1.028e-01  9.159e-02   1.122    0.265
damage:year       -9.458e-05  7.284e-04  -0.130    0.897

Residual standard error: 1.031 on 88 degrees of freedom
Multiple R-squared:  0.5104,    Adjusted R-squared:  0.4882 
F-statistic: 22.94 on 4 and 88 DF,  p-value: 5.281e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3131 -0.7419  0.0328  0.5872  3.4585 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.64108    0.54224  -1.182    0.240    
femininity        -0.72802    0.70866  -1.027    0.307    
damage             0.36593    0.07496   4.882 4.66e-06 ***
femininity:damage  0.09990    0.09153   1.091    0.278    
damage:post       -0.01137    0.02890  -0.394    0.695    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 88 degrees of freedom
Multiple R-squared:  0.5112,    Adjusted R-squared:  0.489 
F-statistic: 23.01 on 4 and 88 DF,  p-value: 4.937e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9364 -0.7960 -0.1618  0.8829  4.1517 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.287e+00  3.040e-01   4.233 5.60e-05 ***
femininity  4.027e-02  4.168e-02   0.966    0.337    
damage      5.231e-05  7.438e-06   7.033 3.98e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 89 degrees of freedom
Multiple R-squared:  0.3592,    Adjusted R-squared:  0.3448 
F-statistic: 24.95 on 2 and 89 DF,  p-value: 2.499e-09
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0300 -0.7994 -0.1757  0.8713  4.1446 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.325e+00  3.174e-01   4.176 6.96e-05 ***
femininity   3.472e-02  4.369e-02   0.795    0.429    
damage       3.492e-04  6.673e-04   0.523    0.602    
damage:year -1.499e-07  3.370e-07  -0.445    0.657    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.15 on 88 degrees of freedom
Multiple R-squared:  0.3607,    Adjusted R-squared:  0.3389 
F-statistic: 16.55 on 3 and 88 DF,  p-value: 1.307e-08
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3852 -0.7865 -0.2003  0.8771  4.1451 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.374e+00  3.144e-01   4.371 3.38e-05 ***
femininity   2.657e-02  4.354e-02   0.610    0.543    
damage       6.015e-05  1.040e-05   5.783 1.10e-07 ***
damage:post -1.434e-05  1.331e-05  -1.077    0.284    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.144 on 88 degrees of freedom
Multiple R-squared:  0.3676,    Adjusted R-squared:  0.346 
F-statistic: 17.05 on 3 and 88 DF,  p-value: 8.179e-09
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4526 -0.8216 -0.1678  0.8706  4.1951 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.467e+00  3.340e-01   4.392 3.12e-05 ***
femininity        9.480e-03  4.799e-02   0.198   0.8439    
damage            3.616e-05  1.463e-05   2.472   0.0154 *  
femininity:damage 2.885e-06  2.253e-06   1.281   0.2037    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.141 on 88 degrees of freedom
Multiple R-squared:  0.371, Adjusted R-squared:  0.3495 
F-statistic:  17.3 on 3 and 88 DF,  p-value: 6.486e-09
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3301 -0.8342 -0.1419  0.8924  4.2257 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.475e+00  3.356e-01   4.395 3.11e-05 ***
femininity         6.348e-03  4.848e-02   0.131    0.896    
damage            -4.909e-04  9.182e-04  -0.535    0.594    
femininity:damage  4.089e-06  3.084e-06   1.326    0.188    
damage:year        2.628e-07  4.578e-07   0.574    0.567    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 87 degrees of freedom
Multiple R-squared:  0.3733,    Adjusted R-squared:  0.3445 
F-statistic: 12.96 on 4 and 87 DF,  p-value: 2.554e-08
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4921 -0.8208 -0.1688  0.8614  4.1869 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.460e+00  3.382e-01   4.317 4.17e-05 ***
femininity         1.090e-02  4.900e-02   0.223    0.824    
damage             4.049e-05  2.980e-05   1.359    0.178    
femininity:damage  2.446e-06  3.471e-06   0.704    0.483    
damage:post       -3.417e-06  2.046e-05  -0.167    0.868    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.147 on 87 degrees of freedom
Multiple R-squared:  0.3712,    Adjusted R-squared:  0.3423 
F-statistic: 12.84 on 4 and 87 DF,  p-value: 2.953e-08
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2517 -0.8356 -0.0032  0.6625  3.5446 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.07808    0.41360  -2.607   0.0107 *  
femininity   0.01153    0.03719   0.310   0.7572    
damage       0.40957    0.04443   9.217 1.33e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.021 on 89 degrees of freedom
Multiple R-squared:   0.49, Adjusted R-squared:  0.4785 
F-statistic: 42.75 on 2 and 89 DF,  p-value: 9.708e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3302 -0.7706  0.0297  0.5876  3.4700 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -1.0479248  0.4185454  -2.504   0.0141 *
femininity   0.0056692  0.0387254   0.146   0.8839  
damage       1.2395133  1.4577542   0.850   0.3975  
damage:year -0.0004184  0.0007345  -0.570   0.5704  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.025 on 88 degrees of freedom
Multiple R-squared:  0.4919,    Adjusted R-squared:  0.4745 
F-statistic: 28.39 on 3 and 88 DF,  p-value: 6.19e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3229 -0.7544  0.0388  0.6072  3.4718 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.033538   0.419703  -2.463   0.0157 *  
femininity   0.004038   0.038818   0.104   0.9174    
damage       0.420526   0.047255   8.899 6.63e-14 ***
damage:post -0.019759   0.028359  -0.697   0.4878    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.024 on 88 degrees of freedom
Multiple R-squared:  0.4928,    Adjusted R-squared:  0.4755 
F-statistic:  28.5 on 3 and 88 DF,  p-value: 5.719e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2581 -0.7772  0.0271  0.6453  3.5167 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.51005    0.80606  -0.633  0.52853   
femininity        -0.08377    0.12184  -0.688  0.49354   
damage             0.33329    0.10297   3.237  0.00171 **
femininity:damage  0.01271    0.01547   0.822  0.41355   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.023 on 88 degrees of freedom
Multiple R-squared:  0.4939,    Adjusted R-squared:  0.4766 
F-statistic: 28.62 on 3 and 88 DF,  p-value: 5.21e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3190 -0.7502  0.0260  0.5873  3.4608 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.5376124  0.8122200  -0.662    0.510
femininity        -0.0797778  0.1227431  -0.650    0.517
damage             0.9900512  1.5006045   0.660    0.511
femininity:damage  0.0115655  0.0157605   0.734    0.465
damage:year       -0.0003276  0.0007467  -0.439    0.662

Residual standard error: 1.028 on 87 degrees of freedom
Multiple R-squared:  0.495, Adjusted R-squared:  0.4718 
F-statistic: 21.32 on 4 and 87 DF,  p-value: 2.794e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3170 -0.7473  0.0259  0.5820  3.4588 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.53526    0.81033  -0.661  0.51065   
femininity        -0.07956    0.12253  -0.649  0.51784   
damage             0.35086    0.10779   3.255  0.00162 **
femininity:damage  0.01131    0.01572   0.720  0.47372   
damage:post       -0.01655    0.02878  -0.575  0.56672   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.027 on 87 degrees of freedom
Multiple R-squared:  0.4958,    Adjusted R-squared:  0.4726 
F-statistic: 21.39 on 4 and 87 DF,  p-value: 2.612e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9393 -0.7662 -0.2189  0.8842  4.1531 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.366e+00  2.233e-01   6.118 2.48e-08 ***
femininity  2.658e-01  2.546e-01   1.044    0.299    
damage      5.226e-05  7.428e-06   7.035 3.94e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.144 on 89 degrees of freedom
Multiple R-squared:  0.3604,    Adjusted R-squared:  0.346 
F-statistic: 25.07 on 2 and 89 DF,  p-value: 2.312e-09
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0107 -0.7380 -0.2137  0.8739  4.1448 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.390e+00  2.310e-01   6.017 3.98e-08 ***
femininity   2.343e-01  2.659e-01   0.881    0.381    
damage       3.402e-04  6.645e-04   0.512    0.610    
damage:year -1.454e-07  3.356e-07  -0.433    0.666    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.149 on 88 degrees of freedom
Multiple R-squared:  0.3617,    Adjusted R-squared:   0.34 
F-statistic: 16.62 on 3 and 88 DF,  p-value: 1.218e-08
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3671 -0.7443 -0.2218  0.8755  4.1433 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.419e+00  2.286e-01   6.206 1.73e-08 ***
femininity   1.861e-01  2.653e-01   0.701    0.485    
damage       5.998e-05  1.040e-05   5.770 1.16e-07 ***
damage:post -1.407e-05  1.327e-05  -1.061    0.292    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.143 on 88 degrees of freedom
Multiple R-squared:  0.3684,    Adjusted R-squared:  0.3469 
F-statistic: 17.11 on 3 and 88 DF,  p-value: 7.715e-09
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4218 -0.7900 -0.1663  0.8502  4.1963 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.478e+00  2.377e-01   6.218 1.65e-08 ***
femininity        6.666e-02  2.942e-01   0.227 0.821307    
damage            4.169e-05  1.085e-05   3.843 0.000229 ***
femininity:damage 1.976e-05  1.483e-05   1.333 0.186132    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 88 degrees of freedom
Multiple R-squared:  0.373, Adjusted R-squared:  0.3516 
F-statistic: 17.45 on 3 and 88 DF,  p-value: 5.635e-09
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2642 -0.8302 -0.1397  0.8629  4.2314 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.480e+00  2.386e-01   6.204 1.81e-08 ***
femininity         4.189e-02  2.979e-01   0.141    0.888    
damage            -5.263e-04  9.046e-04  -0.582    0.562    
femininity:damage  2.834e-05  2.020e-05   1.403    0.164    
damage:year        2.845e-07  4.531e-07   0.628    0.532    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.143 on 87 degrees of freedom
Multiple R-squared:  0.3758,    Adjusted R-squared:  0.3471 
F-statistic:  13.1 on 4 and 87 DF,  p-value: 2.16e-08
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4518 -0.7880 -0.1699  0.8523  4.1910 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.476e+00  2.399e-01   6.153 2.26e-08 ***
femininity         7.247e-02  3.010e-01   0.241   0.8103    
damage             4.378e-05  2.269e-05   1.930   0.0569 .  
femininity:damage  1.800e-05  2.240e-05   0.804   0.4237    
damage:post       -2.100e-06  1.996e-05  -0.105   0.9165    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.145 on 87 degrees of freedom
Multiple R-squared:  0.3731,    Adjusted R-squared:  0.3443 
F-statistic: 12.94 on 4 and 87 DF,  p-value: 2.597e-08
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2406 -0.8136 -0.0026  0.6676  3.5625 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.02110    0.37043  -2.757  0.00709 ** 
femininity   0.02201    0.22815   0.096  0.92337    
damage       0.40984    0.04458   9.194 1.49e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.022 on 89 degrees of freedom
Multiple R-squared:  0.4895,    Adjusted R-squared:  0.478 
F-statistic: 42.67 on 2 and 89 DF,  p-value: 1.014e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3243 -0.7529  0.0185  0.5914  3.4789 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.0014701  0.3729848  -2.685  0.00867 **
femininity  -0.0240298  0.2402393  -0.100  0.92055   
damage       1.3416312  1.4755091   0.909  0.36569   
damage:year -0.0004695  0.0007431  -0.632  0.52916   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.025 on 88 degrees of freedom
Multiple R-squared:  0.4918,    Adjusted R-squared:  0.4745 
F-statistic: 28.39 on 3 and 88 DF,  p-value: 6.225e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3139 -0.7354  0.0271  0.5925  3.4820 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.98729    0.37389  -2.641  0.00979 ** 
femininity  -0.04046    0.24273  -0.167  0.86800    
damage       0.42274    0.04774   8.855 8.15e-14 ***
damage:post -0.02219    0.02892  -0.767  0.44491    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.024 on 88 degrees of freedom
Multiple R-squared:  0.4929,    Adjusted R-squared:  0.4756 
F-statistic: 28.51 on 3 and 88 DF,  p-value: 5.671e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2507 -0.7331  0.0496  0.6384  3.5321 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.63652    0.53784  -1.183    0.240    
femininity        -0.63784    0.70679  -0.902    0.369    
damage             0.35627    0.07027   5.070 2.18e-06 ***
femininity:damage  0.08967    0.09091   0.986    0.327    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.022 on 88 degrees of freedom
Multiple R-squared:  0.4951,    Adjusted R-squared:  0.4779 
F-statistic: 28.76 on 3 and 88 DF,  p-value: 4.698e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3149 -0.7297  0.0161  0.5889  3.4696 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.6511656  0.5410444  -1.204    0.232
femininity        -0.6222977  0.7106129  -0.876    0.384
damage             1.0838902  1.5050115   0.720    0.473
femininity:damage  0.0827051  0.0924381   0.895    0.373
damage:year       -0.0003645  0.0007531  -0.484    0.630

Residual standard error: 1.027 on 87 degrees of freedom
Multiple R-squared:  0.4964,    Adjusted R-squared:  0.4733 
F-statistic: 21.44 on 4 and 87 DF,  p-value: 2.474e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3109 -0.7316  0.0201  0.5848  3.4678 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.64395    0.53981  -1.193    0.236    
femininity        -0.62880    0.70936  -0.886    0.378    
damage             0.37199    0.07476   4.976 3.24e-06 ***
femininity:damage  0.08137    0.09216   0.883    0.380    
damage:post       -0.01851    0.02926  -0.633    0.529    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.026 on 87 degrees of freedom
Multiple R-squared:  0.4974,    Adjusted R-squared:  0.4743 
F-statistic: 21.52 on 4 and 87 DF,  p-value: 2.282e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4373 -0.7483 -0.1765  0.8389  4.2011 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.320e+00  2.990e-01   4.413 2.88e-05 ***
femininity  2.554e-02  4.155e-02   0.615     0.54    
damage      6.162e-05  8.586e-06   7.177 2.14e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.124 on 88 degrees of freedom
Multiple R-squared:  0.3737,    Adjusted R-squared:  0.3595 
F-statistic: 26.26 on 2 and 88 DF,  p-value: 1.14e-09
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3527 -0.7605 -0.1729  0.8472  4.2061 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.303e+00  3.122e-01   4.173 7.09e-05 ***
femininity   2.766e-02  4.309e-02   0.642    0.523    
damage      -7.738e-05  6.894e-04  -0.112    0.911    
damage:year  7.036e-08  3.489e-07   0.202    0.841    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.131 on 87 degrees of freedom
Multiple R-squared:  0.374, Adjusted R-squared:  0.3525 
F-statistic: 17.33 on 3 and 87 DF,  p-value: 6.601e-09
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4591 -0.7494 -0.1760  0.8373  4.2001 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.324e+00  3.121e-01   4.242 5.51e-05 ***
femininity   2.502e-02  4.306e-02   0.581    0.563    
damage       6.191e-05  1.033e-05   5.991 4.58e-08 ***
damage:post -7.669e-07  1.530e-05  -0.050    0.960    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.131 on 87 degrees of freedom
Multiple R-squared:  0.3738,    Adjusted R-squared:  0.3522 
F-statistic: 17.31 on 3 and 87 DF,  p-value: 6.726e-09
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3390 -0.7368 -0.1883  0.8438  4.1954 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.275e+00  3.517e-01   3.626 0.000485 ***
femininity         3.207e-02  4.957e-02   0.647 0.519302    
damage             6.712e-05  2.404e-05   2.792 0.006437 ** 
femininity:damage -7.800e-07  3.184e-06  -0.245 0.807014    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.131 on 87 degrees of freedom
Multiple R-squared:  0.3742,    Adjusted R-squared:  0.3526 
F-statistic: 17.34 on 3 and 87 DF,  p-value: 6.538e-09
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3298 -0.7427 -0.1847  0.8444  4.1980 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.278e+00  3.583e-01   3.566 0.000594 ***
femininity         3.158e-02  5.100e-02   0.619 0.537356    
damage             2.235e-05  9.739e-04   0.023 0.981742    
femininity:damage -6.414e-07  4.399e-06  -0.146 0.884408    
damage:year        2.216e-08  4.820e-07   0.046 0.963432    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 86 degrees of freedom
Multiple R-squared:  0.3742,    Adjusted R-squared:  0.3451 
F-statistic: 12.86 on 4 and 86 DF,  p-value: 3.017e-08
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4027 -0.7632 -0.1963  0.8364  4.1819 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.262e+00  3.568e-01   3.537 0.000655 ***
femininity         3.468e-02  5.072e-02   0.684 0.495976    
damage             7.459e-05  3.625e-05   2.058 0.042666 *  
femininity:damage -1.544e-06  4.229e-06  -0.365 0.715885    
damage:post       -5.616e-06  2.031e-05  -0.276 0.782852    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 86 degrees of freedom
Multiple R-squared:  0.3747,    Adjusted R-squared:  0.3457 
F-statistic: 12.89 on 4 and 86 DF,  p-value: 2.911e-08
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2495 -0.8433 -0.0052  0.6889  3.5472 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.07452    0.41533  -2.587   0.0113 *  
femininity   0.01517    0.03796   0.400   0.6903    
damage       0.40516    0.04538   8.929 5.75e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.026 on 88 degrees of freedom
Multiple R-squared:  0.4791,    Adjusted R-squared:  0.4672 
F-statistic: 40.46 on 2 and 88 DF,  p-value: 3.453e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3305 -0.7639  0.0254  0.6138  3.4701 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -1.0432058  0.4202974  -2.482    0.015 *
femininity   0.0092505  0.0394181   0.235    0.815  
damage       1.2628509  1.4641646   0.863    0.391  
damage:year -0.0004324  0.0007379  -0.586    0.559  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.029 on 87 degrees of freedom
Multiple R-squared:  0.4811,    Adjusted R-squared:  0.4632 
F-statistic: 26.89 on 3 and 87 DF,  p-value: 2.124e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3266 -0.7486  0.0558  0.6038  3.4683 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.025528   0.421431  -2.433    0.017 *  
femininity   0.007537   0.039385   0.191    0.849    
damage       0.416445   0.047903   8.693 1.91e-13 ***
damage:post -0.021503   0.028607  -0.752    0.454    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.028 on 87 degrees of freedom
Multiple R-squared:  0.4824,    Adjusted R-squared:  0.4646 
F-statistic: 27.03 on 3 and 87 DF,  p-value: 1.905e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2564 -0.7576  0.0034  0.6354  3.5133 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.35728    0.83144  -0.430  0.66847   
femininity        -0.10300    0.12460  -0.827  0.41068   
damage             0.30682    0.10868   2.823  0.00589 **
femininity:damage  0.01601    0.01608   0.996  0.32211   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.026 on 87 degrees of freedom
Multiple R-squared:  0.4849,    Adjusted R-squared:  0.4672 
F-statistic:  27.3 on 3 and 87 DF,  p-value: 1.545e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3162 -0.7331  0.0083  0.5805  3.4584 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.3851874  0.8378804  -0.460    0.647
femininity        -0.0989725  0.1255350  -0.788    0.433
damage             0.9526590  1.5049189   0.633    0.528
femininity:damage  0.0148652  0.0163692   0.908    0.366
damage:year       -0.0003221  0.0007485  -0.430    0.668

Residual standard error: 1.03 on 86 degrees of freedom
Multiple R-squared:  0.486, Adjusted R-squared:  0.4621 
F-statistic: 20.33 on 4 and 86 DF,  p-value: 8.129e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3205 -0.7224  0.0188  0.5689  3.4500 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.37699    0.83498  -0.451  0.65277   
femininity        -0.09939    0.12517  -0.794  0.42936   
damage             0.32462    0.11273   2.880  0.00502 **
femininity:damage  0.01465    0.01628   0.900  0.37060   
damage:post       -0.01803    0.02890  -0.624  0.53437   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.029 on 86 degrees of freedom
Multiple R-squared:  0.4873,    Adjusted R-squared:  0.4634 
F-statistic: 20.43 on 4 and 86 DF,  p-value: 7.361e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4306 -0.7649 -0.1654  0.8253  4.1969 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.359e+00  2.193e-01   6.199 1.79e-08 ***
femininity  1.843e-01  2.531e-01   0.728    0.469    
damage      6.157e-05  8.578e-06   7.178 2.14e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.124 on 88 degrees of freedom
Multiple R-squared:  0.3748,    Adjusted R-squared:  0.3606 
F-statistic: 26.38 on 2 and 88 DF,  p-value: 1.057e-09
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3355 -0.7822 -0.1664  0.8358  4.2030 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.346e+00  2.281e-01   5.903 6.72e-08 ***
femininity   1.982e-01  2.620e-01   0.757    0.451    
damage      -9.259e-05  6.875e-04  -0.135    0.893    
damage:year  7.803e-08  3.479e-07   0.224    0.823    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.13 on 87 degrees of freedom
Multiple R-squared:  0.3752,    Adjusted R-squared:  0.3536 
F-statistic: 17.41 on 3 and 87 DF,  p-value: 6.104e-09
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4407 -0.7654 -0.1653  0.8246  4.1964 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.361e+00  2.284e-01   5.957 5.31e-08 ***
femininity   1.828e-01  2.622e-01   0.697    0.488    
damage       6.170e-05  1.032e-05   5.978 4.87e-08 ***
damage:post -3.521e-07  1.528e-05  -0.023    0.982    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.13 on 87 degrees of freedom
Multiple R-squared:  0.3748,    Adjusted R-squared:  0.3533 
F-statistic: 17.39 on 3 and 87 DF,  p-value: 6.255e-09
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4218 -0.7627 -0.1666  0.8270  4.1963 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.357e+00  2.485e-01   5.458 4.48e-07 ***
femininity         1.883e-01  3.023e-01   0.623 0.534908    
damage             6.193e-05  1.691e-05   3.662 0.000429 ***
femininity:damage -4.879e-07  1.966e-05  -0.025 0.980262    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.13 on 87 degrees of freedom
Multiple R-squared:  0.3748,    Adjusted R-squared:  0.3533 
F-statistic: 17.39 on 3 and 87 DF,  p-value: 6.255e-09
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3514 -0.8108 -0.1558  0.8279  4.2120 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.363e+00  2.509e-01   5.432 5.11e-07 ***
femininity         1.720e-01  3.097e-01   0.555    0.580    
damage            -1.925e-04  9.287e-04  -0.207    0.836    
femininity:damage  4.218e-06  2.619e-05   0.161    0.872    
damage:year        1.270e-07  4.636e-07   0.274    0.785    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.136 on 86 degrees of freedom
Multiple R-squared:  0.3754,    Adjusted R-squared:  0.3463 
F-statistic: 12.92 on 4 and 86 DF,  p-value: 2.791e-08
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4358 -0.7604 -0.1682  0.8279  4.1939 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.356e+00  2.505e-01   5.411 5.56e-07 ***
femininity         1.909e-01  3.084e-01   0.619   0.5376    
damage             6.289e-05  2.570e-05   2.447   0.0165 *  
femininity:damage -1.288e-06  2.551e-05  -0.050   0.9599    
damage:post       -9.840e-07  1.982e-05  -0.050   0.9605    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.136 on 86 degrees of freedom
Multiple R-squared:  0.3748,    Adjusted R-squared:  0.3458 
F-statistic: 12.89 on 4 and 86 DF,  p-value: 2.89e-08
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2394 -0.8273  0.0007  0.6867  3.5661 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.00830    0.37293  -2.704  0.00823 ** 
femininity   0.04132    0.23246   0.178  0.85933    
damage       0.40563    0.04558   8.899 6.61e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.026 on 88 degrees of freedom
Multiple R-squared:  0.4783,    Adjusted R-squared:  0.4665 
F-statistic: 40.34 on 2 and 88 DF,  p-value: 3.681e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3252 -0.7559  0.0173  0.6180  3.4805 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.9876455  0.3755394  -2.630   0.0101 *
femininity  -0.0050906  0.2440909  -0.021   0.9834  
damage       1.3609178  1.4822288   0.918   0.3611  
damage:year -0.0004814  0.0007466  -0.645   0.5208  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 87 degrees of freedom
Multiple R-squared:  0.4808,    Adjusted R-squared:  0.4629 
F-statistic: 26.85 on 3 and 87 DF,  p-value: 2.182e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3177 -0.7507  0.0267  0.6101  3.4804 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.97007    0.37657  -2.576   0.0117 *  
femininity  -0.02251    0.24573  -0.092   0.9272    
damage       0.41877    0.04843   8.647 2.37e-13 ***
damage:post -0.02377    0.02917  -0.815   0.4174    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.028 on 87 degrees of freedom
Multiple R-squared:  0.4823,    Adjusted R-squared:  0.4644 
F-statistic: 27.01 on 3 and 87 DF,  p-value: 1.931e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2507 -0.7400  0.0247  0.5989  3.5321 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54721    0.55247  -0.990    0.325    
femininity        -0.72714    0.71874  -1.012    0.314    
damage             0.34007    0.07375   4.611 1.37e-05 ***
femininity:damage  0.10588    0.09372   1.130    0.262    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.025 on 87 degrees of freedom
Multiple R-squared:  0.4859,    Adjusted R-squared:  0.4681 
F-statistic:  27.4 on 3 and 87 DF,  p-value: 1.432e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3144 -0.7361 -0.0140  0.5673  3.4701 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.5620429  0.5557986  -1.011    0.315
femininity        -0.7114274  0.7226878  -0.984    0.328
damage             1.0619881  1.5092756   0.704    0.484
femininity:damage  0.0989079  0.0952561   1.038    0.302
damage:year       -0.0003616  0.0007551  -0.479    0.633

Residual standard error: 1.029 on 86 degrees of freedom
Multiple R-squared:  0.4872,    Adjusted R-squared:  0.4634 
F-statistic: 20.43 on 4 and 86 DF,  p-value: 7.385e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3157 -0.7354  0.0182  0.5646  3.4626 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54994    0.55420  -0.992    0.324    
femininity        -0.72269    0.72100  -1.002    0.319    
damage             0.35608    0.07763   4.587 1.52e-05 ***
femininity:damage  0.09786    0.09475   1.033    0.305    
damage:post       -0.01999    0.02938  -0.681    0.498    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.028 on 86 degrees of freedom
Multiple R-squared:  0.4886,    Adjusted R-squared:  0.4648 
F-statistic: 20.54 on 4 and 86 DF,  p-value: 6.591e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1918 -0.7830 -0.1413  0.8263  4.2045 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.184e+00  2.936e-01   4.031 0.000119 ***
femininity  3.334e-02  4.029e-02   0.828 0.410117    
damage      7.544e-05  9.786e-06   7.709 1.92e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.087 on 87 degrees of freedom
Multiple R-squared:  0.409, Adjusted R-squared:  0.3954 
F-statistic: 30.11 on 2 and 87 DF,  p-value: 1.158e-10
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8698 -0.7611 -0.1044  0.7778  4.1770 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.254e+00  3.004e-01   4.173 7.17e-05 ***
femininity   2.271e-02  4.143e-02   0.548    0.585    
damage       8.753e-04  7.408e-04   1.182    0.241    
damage:year -4.035e-07  3.736e-07  -1.080    0.283    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 86 degrees of freedom
Multiple R-squared:  0.4169,    Adjusted R-squared:  0.3966 
F-statistic:  20.5 on 3 and 86 DF,  p-value: 4.13e-10
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7962 -0.7651 -0.0860  0.7456  4.1723 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.275e+00  2.987e-01   4.270 5.01e-05 ***
femininity   1.943e-02  4.118e-02   0.472    0.638    
damage       8.809e-05  1.309e-05   6.731 1.79e-09 ***
damage:post -2.374e-05  1.645e-05  -1.444    0.152    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.081 on 86 degrees of freedom
Multiple R-squared:  0.423, Adjusted R-squared:  0.4029 
F-statistic: 21.02 on 3 and 86 DF,  p-value: 2.651e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3595 -0.7214 -0.0572  0.7918  4.2290 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.347e+00  3.393e-01   3.969 0.000149 ***
femininity        7.454e-03  4.849e-02   0.154 0.878187    
damage            5.489e-05  2.353e-05   2.332 0.022013 *  
femininity:damage 3.245e-06  3.379e-06   0.960 0.339502    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.088 on 86 degrees of freedom
Multiple R-squared:  0.4153,    Adjusted R-squared:  0.3949 
F-statistic: 20.36 on 3 and 86 DF,  p-value: 4.65e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0412 -0.7434 -0.0767  0.7863  4.1967 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.316e+00  3.442e-01   3.823 0.000251 ***
femininity         1.270e-02  4.940e-02   0.257 0.797755    
damage             6.470e-04  9.600e-04   0.674 0.502151    
femininity:damage  1.619e-06  4.295e-06   0.377 0.707251    
damage:year       -2.935e-07  4.756e-07  -0.617 0.538891    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 85 degrees of freedom
Multiple R-squared:  0.4179,    Adjusted R-squared:  0.3905 
F-statistic: 15.26 on 4 and 85 DF,  p-value: 1.931e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8659 -0.7529 -0.0726  0.7524  4.1804 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.303e+00  3.414e-01   3.818 0.000255 ***
femininity         1.494e-02  4.893e-02   0.305 0.760881    
damage             8.254e-05  3.476e-05   2.374 0.019835 *  
femininity:damage  7.088e-07  4.113e-06   0.172 0.863582    
damage:post       -2.176e-05  2.015e-05  -1.080 0.283313    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.087 on 85 degrees of freedom
Multiple R-squared:  0.4232,    Adjusted R-squared:  0.3961 
F-statistic: 15.59 on 4 and 85 DF,  p-value: 1.325e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2435 -0.8396 -0.0093  0.7188  3.5550 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.05501    0.42238  -2.498   0.0144 *  
femininity   0.01426    0.03827   0.373   0.7103    
damage       0.40285    0.04624   8.712 1.74e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.031 on 87 degrees of freedom
Multiple R-squared:  0.4688,    Adjusted R-squared:  0.4565 
F-statistic: 38.38 on 2 and 87 DF,  p-value: 1.122e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3215 -0.7640  0.0171  0.6487  3.4802 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -1.0310891  0.4263990  -2.418   0.0177 *
femininity   0.0089466  0.0396615   0.226   0.8221  
damage       1.2110229  1.4920562   0.812   0.4192  
damage:year -0.0004071  0.0007513  -0.542   0.5893  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.035 on 86 degrees of freedom
Multiple R-squared:  0.4706,    Adjusted R-squared:  0.4521 
F-statistic: 25.48 on 3 and 86 DF,  p-value: 6.894e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3197 -0.7545  0.0313  0.6327  3.4765 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.01454    0.42739  -2.374   0.0198 *  
femininity   0.00723    0.03963   0.182   0.8557    
damage       0.41449    0.04917   8.429 7.14e-13 ***
damage:post -0.02068    0.02907  -0.711   0.4788    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.034 on 86 degrees of freedom
Multiple R-squared:  0.4719,    Adjusted R-squared:  0.4534 
F-statistic: 25.61 on 3 and 86 DF,  p-value: 6.211e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2533 -0.7657  0.0001  0.6455  3.5180 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.36510    0.83788  -0.436  0.66411   
femininity        -0.10059    0.12639  -0.796  0.42829   
damage             0.30807    0.10963   2.810  0.00613 **
femininity:damage  0.01562    0.01638   0.954  0.34298   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.031 on 86 degrees of freedom
Multiple R-squared:  0.4743,    Adjusted R-squared:  0.456 
F-statistic: 25.87 on 3 and 86 DF,  p-value: 5.098e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3126 -0.7548 -0.0149  0.5877  3.4627 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.3892141  0.8439846  -0.461    0.646
femininity        -0.0976042  0.1272096  -0.767    0.445
damage             0.9352789  1.5263766   0.613    0.542
femininity:damage  0.0146598  0.0166274   0.882    0.380
damage:year       -0.0003130  0.0007598  -0.412    0.681

Residual standard error: 1.036 on 85 degrees of freedom
Multiple R-squared:  0.4754,    Adjusted R-squared:  0.4507 
F-statistic: 19.25 on 4 and 85 DF,  p-value: 2.632e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3181 -0.7412 -0.0093  0.5725  3.4531 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.38043    0.84135  -0.452  0.65230   
femininity        -0.09829    0.12691  -0.775  0.44078   
damage             0.32497    0.11350   2.863  0.00528 **
femininity:damage  0.01449    0.01655   0.875  0.38383   
damage:post       -0.01778    0.02930  -0.607  0.54557   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.035 on 85 degrees of freedom
Multiple R-squared:  0.4766,    Adjusted R-squared:  0.452 
F-statistic: 19.35 on 4 and 85 DF,  p-value: 2.39e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1944 -0.7802 -0.1214  0.7720  4.2063 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.251e+00  2.160e-01   5.791 1.09e-07 ***
femininity  2.179e-01  2.451e-01   0.889    0.376    
damage      7.535e-05  9.782e-06   7.703 1.98e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.087 on 87 degrees of freedom
Multiple R-squared:  0.4097,    Adjusted R-squared:  0.3962 
F-statistic: 30.19 on 2 and 87 DF,  p-value: 1.099e-10
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8775 -0.7646 -0.0967  0.7326  4.1773 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.296e+00  2.199e-01   5.892 7.24e-08 ***
femininity   1.536e-01  2.523e-01   0.609    0.544    
damage       8.637e-04  7.409e-04   1.166    0.247    
damage:year -3.976e-07  3.736e-07  -1.064    0.290    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 86 degrees of freedom
Multiple R-squared:  0.4174,    Adjusted R-squared:  0.3971 
F-statistic: 20.54 on 3 and 86 DF,  p-value: 3.991e-10
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8031 -0.7576 -0.0851  0.7075  4.1729 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.312e+00  2.190e-01   5.992 4.71e-08 ***
femininity   1.308e-01  2.513e-01   0.520    0.604    
damage       8.788e-05  1.312e-05   6.700 2.06e-09 ***
damage:post -2.347e-05  1.648e-05  -1.424    0.158    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.08 on 86 degrees of freedom
Multiple R-squared:  0.4233,    Adjusted R-squared:  0.4032 
F-statistic: 21.04 on 3 and 86 DF,  p-value: 2.589e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3850 -0.7221 -0.0500  0.7659  4.2335 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.357e+00  2.389e-01   5.678 1.81e-07 ***
femininity        4.902e-02  2.946e-01   0.166 0.868251    
damage            6.193e-05  1.626e-05   3.809 0.000261 ***
femininity:damage 2.101e-05  2.035e-05   1.032 0.304751    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 86 degrees of freedom
Multiple R-squared:  0.4169,    Adjusted R-squared:  0.3966 
F-statistic:  20.5 on 3 and 86 DF,  p-value: 4.122e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1013 -0.7339 -0.0690  0.7496  4.2036 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.344e+00  2.409e-01   5.578 2.83e-07 ***
femininity         7.420e-02  2.992e-01   0.248    0.805    
damage             5.841e-04  9.315e-04   0.627    0.532    
femininity:damage  1.263e-05  2.531e-05   0.499    0.619    
damage:year       -2.607e-07  4.649e-07  -0.561    0.577    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.091 on 85 degrees of freedom
Multiple R-squared:  0.4191,    Adjusted R-squared:  0.3918 
F-statistic: 15.33 on 4 and 85 DF,  p-value: 1.773e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9220 -0.7556 -0.0681  0.7199  4.1867 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.339e+00  2.395e-01   5.593 2.66e-07 ***
femininity         8.547e-02  2.968e-01   0.288  0.77407    
damage             8.159e-05  2.533e-05   3.221  0.00181 ** 
femininity:damage  7.135e-06  2.454e-05   0.291  0.77192    
damage:post       -2.022e-05  1.998e-05  -1.012  0.31438    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.086 on 85 degrees of freedom
Multiple R-squared:  0.4239,    Adjusted R-squared:  0.3968 
F-statistic: 15.64 on 4 and 85 DF,  p-value: 1.261e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2332 -0.8384  0.0048  0.6970  3.5737 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.99098    0.37860  -2.617   0.0104 *  
femininity   0.03725    0.23399   0.159   0.8739    
damage       0.40317    0.04643   8.682 2.01e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.032 on 87 degrees of freedom
Multiple R-squared:  0.4681,    Adjusted R-squared:  0.4558 
F-statistic: 38.28 on 2 and 87 DF,  p-value: 1.188e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3160 -0.7687  0.0218  0.6556  3.4907 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.9767899  0.3807562  -2.565    0.012 *
femininity  -0.0052573  0.2454367  -0.021    0.983  
damage       1.3043889  1.5119561   0.863    0.391  
damage:year -0.0004538  0.0007610  -0.596    0.553  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.035 on 86 degrees of freedom
Multiple R-squared:  0.4703,    Adjusted R-squared:  0.4518 
F-statistic: 25.45 on 3 and 86 DF,  p-value: 7.068e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3107 -0.7654  0.0243  0.6404  3.4885 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.96043    0.38155  -2.517   0.0137 *  
femininity  -0.02267    0.24709  -0.092   0.9271    
damage       0.41669    0.04974   8.377 9.11e-13 ***
damage:post -0.02286    0.02966  -0.771   0.4431    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.034 on 86 degrees of freedom
Multiple R-squared:  0.4717,    Adjusted R-squared:  0.4533 
F-statistic:  25.6 on 3 and 86 DF,  p-value: 6.288e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2469 -0.7412 -0.0011  0.6124  3.5371 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54721    0.55557  -0.985    0.327    
femininity        -0.71321    0.72684  -0.981    0.329    
damage             0.34007    0.07416   4.585 1.53e-05 ***
femininity:damage  0.10364    0.09505   1.090    0.279    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.03 on 86 degrees of freedom
Multiple R-squared:  0.4753,    Adjusted R-squared:  0.457 
F-statistic: 25.97 on 3 and 86 DF,  p-value: 4.701e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3098 -0.7383 -0.0218  0.5807  3.4754 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.5615261  0.5590363  -1.004    0.318
femininity        -0.7032352  0.7305429  -0.963    0.338
damage             1.0368295  1.5345414   0.676    0.501
femininity:damage  0.0977489  0.0963648   1.014    0.313
damage:year       -0.0003490  0.0007678  -0.455    0.651

Residual standard error: 1.035 on 85 degrees of freedom
Multiple R-squared:  0.4766,    Adjusted R-squared:  0.452 
F-statistic: 19.35 on 4 and 85 DF,  p-value: 2.388e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3126 -0.7395  0.0087  0.5766  3.4664 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54989    0.55742  -0.986    0.327    
femininity        -0.71577    0.72926  -0.982    0.329    
damage             0.35579    0.07815   4.553 1.75e-05 ***
femininity:damage  0.09689    0.09591   1.010    0.315    
damage:post       -0.01963    0.02983  -0.658    0.512    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.034 on 85 degrees of freedom
Multiple R-squared:  0.478, Adjusted R-squared:  0.4534 
F-statistic: 19.46 on 4 and 85 DF,  p-value: 2.139e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8729 -0.7717 -0.1632  0.8794  2.5164 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.331e+00  2.788e-01   4.776 7.00e-06 ***
femininity  2.658e-02  3.853e-02   0.690    0.492    
damage      5.138e-05  6.255e-06   8.215 1.57e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.057 on 89 degrees of freedom
Multiple R-squared:  0.4325,    Adjusted R-squared:  0.4197 
F-statistic: 33.91 on 2 and 89 DF,  p-value: 1.128e-11
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0070 -0.7759 -0.1742  0.8569  2.4455 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.363e+00  2.833e-01   4.812 6.15e-06 ***
femininity   2.105e-02  3.944e-02   0.534    0.595    
damage       4.057e-04  5.085e-04   0.798    0.427    
damage:year -1.784e-07  2.560e-07  -0.697    0.488    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.061 on 88 degrees of freedom
Multiple R-squared:  0.4356,    Adjusted R-squared:  0.4164 
F-statistic: 22.64 on 3 and 88 DF,  p-value: 5.937e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3523 -0.7691 -0.1368  0.8716  2.2887 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.404e+00  2.834e-01   4.955 3.47e-06 ***
femininity   1.379e-02  3.964e-02   0.348    0.729    
damage       6.079e-05  9.579e-06   6.346 9.32e-09 ***
damage:post -1.473e-05  1.139e-05  -1.293    0.199    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.054 on 88 degrees of freedom
Multiple R-squared:  0.4431,    Adjusted R-squared:  0.4241 
F-statistic: 23.33 on 3 and 88 DF,  p-value: 3.332e-11
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1499 -0.8088 -0.1218  0.8328  2.3868 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.502e+00  3.087e-01   4.868 4.93e-06 ***
femininity        -1.591e-03  4.432e-02  -0.036  0.97145    
damage             3.615e-05  1.350e-05   2.677  0.00886 ** 
femininity:damage  2.541e-06  1.997e-06   1.272  0.20666    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.054 on 88 degrees of freedom
Multiple R-squared:  0.4427,    Adjusted R-squared:  0.4237 
F-statistic:  23.3 on 3 and 88 DF,  p-value: 3.418e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2408 -0.8021 -0.1208  0.8298  2.3706 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.501e+00  3.104e-01   4.836 5.67e-06 ***
femininity        -1.466e-03  4.456e-02  -0.033    0.974    
damage             1.674e-04  5.537e-04   0.302    0.763    
femininity:damage  2.346e-06  2.168e-06   1.082    0.282    
damage:year       -6.548e-08  2.762e-07  -0.237    0.813    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.06 on 87 degrees of freedom
Multiple R-squared:  0.4431,    Adjusted R-squared:  0.4175 
F-statistic:  17.3 on 4 and 87 DF,  p-value: 1.773e-10
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4157 -0.7980 -0.1140  0.8686  2.2877 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.485e+00  3.106e-01   4.782 7.02e-06 ***
femininity         7.269e-04  4.458e-02   0.016   0.9870    
damage             4.800e-05  2.194e-05   2.188   0.0314 *  
femininity:damage  1.582e-06  2.441e-06   0.648   0.5185    
damage:post       -9.565e-06  1.393e-05  -0.687   0.4941    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.057 on 87 degrees of freedom
Multiple R-squared:  0.4457,    Adjusted R-squared:  0.4202 
F-statistic: 17.49 on 4 and 87 DF,  p-value: 1.449e-10
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21829 -0.79120  0.01354  0.69150  2.35338 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.084216   0.386446  -2.806  0.00617 ** 
femininity   0.003939   0.035069   0.112  0.91082    
damage       0.413804   0.041311  10.017 2.96e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9614 on 89 degrees of freedom
Multiple R-squared:  0.5309,    Adjusted R-squared:  0.5204 
F-statistic: 50.37 on 2 and 89 DF,  p-value: 2.346e-15
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18511 -0.80469 -0.00067  0.70300  2.37292 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.0926897  0.3899778  -2.802  0.00625 **
femininity   0.0061170  0.0363191   0.168  0.86664   
damage       0.0779617  1.3460277   0.058  0.95394   
damage:year  0.0001691  0.0006773   0.250  0.80346   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9665 on 88 degrees of freedom
Multiple R-squared:  0.5313,    Adjusted R-squared:  0.5153 
F-statistic: 33.25 on 3 and 88 DF,  p-value: 1.844e-14
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.23009 -0.78184  0.02572  0.67971  2.33814 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.078300   0.391818  -2.752  0.00719 ** 
femininity   0.002813   0.036532   0.077  0.93881    
damage       0.415688   0.044498   9.342 8.12e-15 ***
damage:post -0.003141   0.026586  -0.118  0.90623    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9668 on 88 degrees of freedom
Multiple R-squared:  0.531, Adjusted R-squared:  0.515 
F-statistic: 33.21 on 3 and 88 DF,  p-value: 1.889e-14
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22381 -0.73966  0.06966  0.67468  2.25590 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.49352    0.75785  -0.651  0.51661    
femininity        -0.09454    0.11418  -0.828  0.40991    
damage             0.33445    0.09682   3.454  0.00085 ***
femininity:damage  0.01312    0.01448   0.906  0.36720    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9624 on 88 degrees of freedom
Multiple R-squared:  0.5353,    Adjusted R-squared:  0.5194 
F-statistic: 33.78 on 3 and 88 DF,  p-value: 1.269e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.17427 -0.75689  0.07149  0.71270  2.27992 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.4739390  0.7634129  -0.621    0.536
femininity        -0.0966551  0.1148826  -0.841    0.402
damage            -0.1743466  1.3731931  -0.127    0.899
femininity:damage  0.0138411  0.0146776   0.943    0.348
damage:year        0.0002539  0.0006836   0.371    0.711

Residual standard error: 0.9671 on 87 degrees of freedom
Multiple R-squared:  0.536, Adjusted R-squared:  0.5147 
F-statistic: 25.12 on 4 and 87 DF,  p-value: 7.576e-14
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22357 -0.73963  0.06966  0.67494  2.25618 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -4.934e-01  7.631e-01  -0.647   0.5196   
femininity        -9.455e-02  1.149e-01  -0.823   0.4130   
damage             3.344e-01  1.013e-01   3.301   0.0014 **
femininity:damage  1.313e-02  1.469e-02   0.893   0.3741   
damage:post        6.359e-05  2.686e-02   0.002   0.9981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9679 on 87 degrees of freedom
Multiple R-squared:  0.5353,    Adjusted R-squared:  0.5139 
F-statistic: 25.05 on 4 and 87 DF,  p-value: 8.106e-14
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8642 -0.7372 -0.1798  0.8560  2.5227 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.376e+00  2.040e-01   6.745 1.49e-09 ***
femininity  1.869e-01  2.350e-01   0.795    0.428    
damage      5.132e-05  6.249e-06   8.213 1.59e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.057 on 89 degrees of freedom
Multiple R-squared:  0.4335,    Adjusted R-squared:  0.4207 
F-statistic: 34.05 on 2 and 89 DF,  p-value: 1.043e-11
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9953 -0.7297 -0.1696  0.8436  2.4493 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.393e+00  2.061e-01   6.758 1.46e-09 ***
femininity   1.564e-01  2.398e-01   0.652    0.516    
damage       3.990e-04  5.065e-04   0.788    0.433    
damage:year -1.750e-07  2.550e-07  -0.686    0.494    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.06 on 88 degrees of freedom
Multiple R-squared:  0.4365,    Adjusted R-squared:  0.4173 
F-statistic: 22.72 on 3 and 88 DF,  p-value: 5.542e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3386 -0.7325 -0.1497  0.8723  2.2914 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.416e+00  2.057e-01   6.883 8.26e-10 ***
femininity   1.140e-01  2.411e-01   0.473    0.637    
damage       6.057e-05  9.575e-06   6.326 1.02e-08 ***
damage:post -1.444e-05  1.135e-05  -1.272    0.207    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.053 on 88 degrees of freedom
Multiple R-squared:  0.4437,    Adjusted R-squared:  0.4247 
F-statistic:  23.4 on 3 and 88 DF,  p-value: 3.168e-11
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0557 -0.7964 -0.1252  0.8411  2.4148 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.478e+00  2.199e-01   6.722 1.72e-09 ***
femininity        2.010e-02  2.710e-01   0.074    0.941    
damage            4.169e-05  1.004e-05   4.154 7.54e-05 ***
femininity:damage 1.568e-05  1.280e-05   1.225    0.224    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.054 on 88 degrees of freedom
Multiple R-squared:  0.443, Adjusted R-squared:  0.424 
F-statistic: 23.33 on 3 and 88 DF,  p-value: 3.356e-11
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1900 -0.7946 -0.1197  0.8354  2.3883 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.478e+00  2.211e-01   6.684 2.12e-09 ***
femininity         1.974e-02  2.724e-01   0.072    0.942    
damage             2.121e-04  5.363e-04   0.396    0.693    
femininity:damage  1.431e-05  1.356e-05   1.055    0.294    
damage:year       -8.537e-08  2.686e-07  -0.318    0.751    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.059 on 87 degrees of freedom
Multiple R-squared:  0.4436,    Adjusted R-squared:  0.418 
F-statistic: 17.34 on 4 and 87 DF,  p-value: 1.704e-10
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3809 -0.7808 -0.1202  0.8411  2.2955 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.468e+00  2.209e-01   6.648  2.5e-09 ***
femininity         3.112e-02  2.721e-01   0.114  0.90923    
damage             5.156e-05  1.661e-05   3.103  0.00258 ** 
femininity:damage  9.940e-06  1.496e-05   0.665  0.50808    
damage:post       -9.911e-06  1.327e-05  -0.747  0.45711    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.056 on 87 degrees of freedom
Multiple R-squared:  0.4465,    Adjusted R-squared:  0.4211 
F-statistic: 17.55 on 4 and 87 DF,  p-value: 1.366e-10
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.20749 -0.77632  0.00704  0.70042  2.36645 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.05316    0.34544  -3.049  0.00303 ** 
femininity  -0.01431    0.21479  -0.067  0.94702    
damage       0.41431    0.04144   9.997 3.25e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9614 on 89 degrees of freedom
Multiple R-squared:  0.5309,    Adjusted R-squared:  0.5203 
F-statistic: 50.36 on 2 and 89 DF,  p-value: 2.355e-15
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18115 -0.79137  0.00837  0.70889  2.38334 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -1.0562941  0.3476478  -3.038  0.00313 **
femininity  -0.0015946  0.2246814  -0.007  0.99435   
damage       0.1354385  1.3602044   0.100  0.92091   
damage:year  0.0001403  0.0006841   0.205  0.83796   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9666 on 88 degrees of freedom
Multiple R-squared:  0.5311,    Adjusted R-squared:  0.5151 
F-statistic: 33.23 on 3 and 88 DF,  p-value: 1.87e-14
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2238 -0.7624  0.0227  0.6843  2.3432 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.047560   0.348837  -3.003  0.00348 ** 
femininity  -0.026898   0.227848  -0.118  0.90629    
damage       0.417249   0.044997   9.273 1.13e-14 ***
damage:post -0.004693   0.027075  -0.173  0.86277    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9667 on 88 degrees of freedom
Multiple R-squared:  0.531, Adjusted R-squared:  0.5151 
F-statistic: 33.22 on 3 and 88 DF,  p-value: 1.882e-14
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21631 -0.71866  0.08346  0.65840  2.26913 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.63652    0.50514  -1.260    0.211    
femininity        -0.71937    0.66033  -1.089    0.279    
damage             0.35627    0.06600   5.398 5.63e-07 ***
femininity:damage  0.09564    0.08472   1.129    0.262    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.96 on 88 degrees of freedom
Multiple R-squared:  0.5376,    Adjusted R-squared:  0.5218 
F-statistic:  34.1 on 3 and 88 DF,  p-value: 1.021e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.17313 -0.74898  0.08838  0.64232  2.29363 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.6272109  0.5084550  -1.234    0.221
femininity        -0.7228708  0.6637622  -1.089    0.279
damage            -0.1060436  1.3736555  -0.077    0.939
femininity:damage  0.0989635  0.0857169   1.155    0.251
damage:year        0.0002316  0.0006874   0.337    0.737

Residual standard error: 0.9648 on 87 degrees of freedom
Multiple R-squared:  0.5382,    Adjusted R-squared:  0.5169 
F-statistic: 25.35 on 4 and 87 DF,  p-value: 6.196e-14
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22037 -0.71531  0.08359  0.65421  2.26372 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.636992   0.508147  -1.254    0.213    
femininity        -0.719349   0.664107  -1.083    0.282    
damage             0.357271   0.070284   5.083  2.1e-06 ***
femininity:damage  0.095208   0.085784   1.110    0.270    
damage:post       -0.001182   0.027224  -0.043    0.965    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9654 on 87 degrees of freedom
Multiple R-squared:  0.5376,    Adjusted R-squared:  0.5163 
F-statistic: 25.29 on 4 and 87 DF,  p-value: 6.544e-14
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0114 -0.7856 -0.1292  0.8934  2.4737 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.310e+00  2.819e-01   4.648 1.17e-05 ***
femininity  2.819e-02  3.875e-02   0.727    0.469    
damage      5.312e-05  6.897e-06   7.702 1.88e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.061 on 88 degrees of freedom
Multiple R-squared:  0.4033,    Adjusted R-squared:  0.3898 
F-statistic: 29.74 on 2 and 88 DF,  p-value: 1.355e-10
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9842 -0.7903 -0.1737  0.8577  2.4415 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.344e+00  2.942e-01   4.568 1.61e-05 ***
femininity   2.329e-02  4.060e-02   0.574    0.568    
damage       3.162e-04  6.187e-04   0.511    0.611    
damage:year -1.329e-07  3.125e-07  -0.425    0.672    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.066 on 87 degrees of freedom
Multiple R-squared:  0.4046,    Adjusted R-squared:  0.384 
F-statistic:  19.7 on 3 and 87 DF,  p-value: 7.778e-10
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3542 -0.7788 -0.1463  0.8840  2.2879 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.396e+00  2.912e-01   4.793 6.72e-06 ***
femininity   1.477e-02  4.044e-02   0.365    0.716    
damage       6.081e-05  9.635e-06   6.312 1.12e-08 ***
damage:post -1.407e-05  1.233e-05  -1.141    0.257    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.059 on 87 degrees of freedom
Multiple R-squared:  0.4121,    Adjusted R-squared:  0.3919 
F-statistic: 20.33 on 3 and 87 DF,  p-value: 4.498e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4883 -0.7914 -0.1100  0.8548  2.2826 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.506e+00  3.086e-01   4.880 4.76e-06 ***
femininity        -5.411e-03  4.447e-02  -0.122    0.903    
damage             3.557e-05  1.351e-05   2.633    0.010 *  
femininity:damage  3.136e-06  2.081e-06   1.507    0.136    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.054 on 87 degrees of freedom
Multiple R-squared:  0.4185,    Adjusted R-squared:  0.3985 
F-statistic: 20.87 on 3 and 87 DF,  p-value: 2.822e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.32887 -0.78564 -0.07467  0.85591  2.26969 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.517e+00  3.095e-01   4.901 4.45e-06 ***
femininity        -9.638e-03  4.487e-02  -0.215    0.830    
damage            -6.521e-04  8.474e-04  -0.770    0.444    
femininity:damage  4.710e-06  2.848e-06   1.654    0.102    
damage:year        3.429e-07  4.225e-07   0.812    0.419    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.056 on 86 degrees of freedom
Multiple R-squared:  0.4229,    Adjusted R-squared:  0.3961 
F-statistic: 15.76 on 4 and 86 DF,  p-value: 1.037e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4901 -0.7915 -0.1102  0.8550  2.2818 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.506e+00  3.127e-01   4.816 6.22e-06 ***
femininity        -5.346e-03  4.545e-02  -0.118    0.907    
damage             3.577e-05  2.755e-05   1.298    0.198    
femininity:damage  3.116e-06  3.211e-06   0.970    0.335    
damage:post       -1.531e-07  1.892e-05  -0.008    0.994    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.06 on 86 degrees of freedom
Multiple R-squared:  0.4185,    Adjusted R-squared:  0.3915 
F-statistic: 15.47 on 4 and 86 DF,  p-value: 1.426e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1934 -0.7861  0.0246  0.6921  2.4050 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.005704   0.386686  -2.601   0.0109 *  
femininity   0.001288   0.034833   0.037   0.9706    
damage       0.403412   0.041524   9.715 1.38e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9538 on 88 degrees of freedom
Multiple R-squared:  0.5181,    Adjusted R-squared:  0.5071 
F-statistic:  47.3 on 2 and 88 DF,  p-value: 1.127e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21042 -0.77844  0.03379  0.67428  2.39578 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -9.996e-01  3.917e-01  -2.552   0.0125 *
femininity   8.563e-05  3.626e-02   0.002   0.9981  
damage       5.801e-01  1.375e+00   0.422   0.6742  
damage:year -8.907e-05  6.929e-04  -0.129   0.8980  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9592 on 87 degrees of freedom
Multiple R-squared:  0.5181,    Adjusted R-squared:  0.5015 
F-statistic: 31.18 on 3 and 87 DF,  p-value: 8.786e-14
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22880 -0.76279  0.04973  0.65440  2.36003 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.984669   0.392934  -2.506   0.0141 *  
femininity  -0.002270   0.036362  -0.062   0.9504    
damage       0.408825   0.044330   9.222 1.57e-14 ***
damage:post -0.009653   0.026677  -0.362   0.7184    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9585 on 87 degrees of freedom
Multiple R-squared:  0.5188,    Adjusted R-squared:  0.5022 
F-statistic: 31.26 on 3 and 87 DF,  p-value: 8.303e-14
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19930 -0.75676  0.02321  0.68723  2.32134 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.51865    0.75314  -0.689 0.492878    
femininity        -0.08044    0.11385  -0.707 0.481697    
damage             0.33798    0.09622   3.512 0.000706 ***
femininity:damage  0.01091    0.01446   0.754 0.452716    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9561 on 87 degrees of freedom
Multiple R-squared:  0.5212,    Adjusted R-squared:  0.5047 
F-statistic: 31.57 on 3 and 87 DF,  p-value: 6.691e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2001 -0.7564  0.0227  0.6864  2.3210 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -5.190e-01  7.598e-01  -0.683    0.496
femininity        -8.039e-02  1.148e-01  -0.700    0.486
damage             3.468e-01  1.415e+00   0.245    0.807
femininity:damage  1.089e-02  1.474e-02   0.739    0.462
damage:year       -4.399e-06  7.041e-04  -0.006    0.995

Residual standard error: 0.9617 on 86 degrees of freedom
Multiple R-squared:  0.5212,    Adjusted R-squared:  0.4989 
F-statistic:  23.4 on 4 and 86 DF,  p-value: 4.135e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22374 -0.74583  0.01635  0.66108  2.29415 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.528875   0.758340  -0.697 0.487427    
femininity        -0.078748   0.114666  -0.687 0.494081    
damage             0.345115   0.100887   3.421 0.000957 ***
femininity:damage  0.010351   0.014714   0.703 0.483652    
damage:post       -0.006754   0.027071  -0.249 0.803570    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9613 on 86 degrees of freedom
Multiple R-squared:  0.5215,    Adjusted R-squared:  0.4993 
F-statistic: 23.44 on 4 and 86 DF,  p-value: 4.011e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0060 -0.7685 -0.1697  0.8633  2.4791 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.357e+00  2.069e-01   6.558 3.61e-09 ***
femininity  1.987e-01  2.365e-01   0.840    0.403    
damage      5.310e-05  6.888e-06   7.709 1.81e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.06 on 88 degrees of freedom
Multiple R-squared:  0.4045,    Adjusted R-squared:  0.391 
F-statistic: 29.89 on 2 and 88 DF,  p-value: 1.242e-10
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9690 -0.7688 -0.1695  0.8524  2.4461 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.378e+00  2.141e-01   6.433 6.55e-09 ***
femininity   1.717e-01  2.470e-01   0.695    0.489    
damage       3.003e-04  6.161e-04   0.488    0.627    
damage:year -1.249e-07  3.111e-07  -0.401    0.689    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.065 on 87 degrees of freedom
Multiple R-squared:  0.4056,    Adjusted R-squared:  0.3851 
F-statistic: 19.79 on 3 and 87 DF,  p-value: 7.213e-10
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3403 -0.7400 -0.1496  0.8731  2.2911 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.408e+00  2.118e-01   6.651 2.46e-09 ***
femininity   1.213e-01  2.462e-01   0.493    0.624    
damage       6.060e-05  9.629e-06   6.293 1.22e-08 ***
damage:post -1.367e-05  1.229e-05  -1.113    0.269    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.059 on 87 degrees of freedom
Multiple R-squared:  0.4129,    Adjusted R-squared:  0.3926 
F-statistic: 20.39 on 3 and 87 DF,  p-value: 4.263e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4619 -0.7807 -0.1007  0.8503  2.2905 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.478e+00  2.195e-01   6.734  1.7e-09 ***
femininity        -1.723e-02  2.725e-01  -0.063    0.950    
damage             4.169e-05  1.002e-05   4.162  7.4e-05 ***
femininity:damage  2.134e-05  1.370e-05   1.558    0.123    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.052 on 87 degrees of freedom
Multiple R-squared:  0.4207,    Adjusted R-squared:  0.4007 
F-statistic: 21.06 on 3 and 87 DF,  p-value: 2.402e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.25835 -0.78607 -0.06969  0.87559  2.28948 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.481e+00  2.198e-01   6.736 1.75e-09 ***
femininity        -5.017e-02  2.754e-01  -0.182   0.8559    
damage            -6.931e-04  8.345e-04  -0.831   0.4085    
femininity:damage  3.246e-05  1.864e-05   1.741   0.0852 .  
damage:year        3.681e-07  4.180e-07   0.881   0.3810    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.053 on 86 degrees of freedom
Multiple R-squared:  0.4259,    Adjusted R-squared:  0.3992 
F-statistic: 15.95 on 4 and 86 DF,  p-value: 8.383e-10
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4449 -0.7803 -0.1016  0.8514  2.2980 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.479e+00  2.215e-01   6.678 2.28e-09 ***
femininity        -2.058e-02  2.790e-01  -0.074   0.9414    
damage             4.050e-05  2.097e-05   1.932   0.0567 .  
femininity:damage  2.234e-05  2.071e-05   1.079   0.2837    
damage:post        1.189e-06  1.845e-05   0.064   0.9488    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.058 on 86 degrees of freedom
Multiple R-squared:  0.4207,    Adjusted R-squared:  0.3938 
F-statistic: 15.61 on 4 and 86 DF,  p-value: 1.217e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18046 -0.77122 -0.00139  0.70115  2.41765 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.97943    0.34585  -2.832  0.00573 ** 
femininity  -0.03389    0.21342  -0.159  0.87420    
damage       0.40404    0.04163   9.706 1.44e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9536 on 88 degrees of freedom
Multiple R-squared:  0.5182,    Adjusted R-squared:  0.5072 
F-statistic: 47.32 on 2 and 88 DF,  p-value: 1.113e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.20451 -0.75950  0.00986  0.68001  2.40329 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.9741636  0.3488771  -2.792  0.00643 **
femininity  -0.0465182  0.2247436  -0.207  0.83651   
damage       0.6672278  1.3918714   0.479  0.63288   
damage:year -0.0001326  0.0007009  -0.189  0.85039   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9589 on 87 degrees of freedom
Multiple R-squared:  0.5184,    Adjusted R-squared:  0.5018 
F-statistic: 31.21 on 3 and 87 DF,  p-value: 8.602e-14
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22026 -0.74156  0.02246  0.66413  2.36151 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.96191    0.34978  -2.750  0.00725 ** 
femininity  -0.06652    0.22715  -0.293  0.77035    
damage       0.41098    0.04476   9.181 1.91e-14 ***
damage:post -0.01183    0.02720  -0.435  0.66460    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9581 on 87 degrees of freedom
Multiple R-squared:  0.5192,    Adjusted R-squared:  0.5027 
F-statistic: 31.32 on 3 and 87 DF,  p-value: 7.974e-14
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18995 -0.70468  0.05119  0.66001  2.33223 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.63652    0.50214  -1.268    0.208    
femininity        -0.62236    0.65989  -0.943    0.348    
damage             0.35627    0.06560   5.431 5.03e-07 ***
femininity:damage  0.08003    0.08492   0.942    0.349    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9543 on 87 degrees of freedom
Multiple R-squared:  0.5231,    Adjusted R-squared:  0.5066 
F-statistic:  31.8 on 3 and 87 DF,  p-value: 5.654e-14
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19580 -0.70273  0.04766  0.65771  2.32934 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -6.378e-01  5.059e-01  -1.261    0.211
femininity        -6.210e-01  6.644e-01  -0.935    0.353
damage             4.214e-01  1.419e+00   0.297    0.767
femininity:damage  7.942e-02  8.643e-02   0.919    0.361
damage:year       -3.265e-05  7.099e-04  -0.046    0.963

Residual standard error: 0.9598 on 86 degrees of freedom
Multiple R-squared:  0.5231,    Adjusted R-squared:  0.5009 
F-statistic: 23.58 on 4 and 86 DF,  p-value: 3.503e-13
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21783 -0.70434  0.03556  0.64832  2.29623 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.639899   0.504899  -1.267    0.208    
femininity        -0.618376   0.663482  -0.932    0.354    
damage             0.363417   0.069962   5.195 1.36e-06 ***
femininity:damage  0.076333   0.086213   0.885    0.378    
damage:post       -0.008419   0.027501  -0.306    0.760    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9593 on 86 degrees of freedom
Multiple R-squared:  0.5236,    Adjusted R-squared:  0.5014 
F-statistic: 23.63 on 4 and 86 DF,  p-value: 3.35e-13
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.44147 -0.70756 -0.09223  0.80975  2.30000 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.345e+00  2.755e-01   4.882 4.73e-06 ***
femininity  1.273e-02  3.839e-02   0.332    0.741    
damage      6.281e-05  7.912e-06   7.939 6.58e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.036 on 87 degrees of freedom
Multiple R-squared:  0.4225,    Adjusted R-squared:  0.4092 
F-statistic: 31.82 on 2 and 87 DF,  p-value: 4.256e-11
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.32259 -0.73668 -0.09668  0.82313  2.31630 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.321e+00  2.874e-01   4.595 1.47e-05 ***
femininity   1.570e-02  3.978e-02   0.395    0.694    
damage      -1.326e-04  6.349e-04  -0.209    0.835    
damage:year  9.890e-08  3.213e-07   0.308    0.759    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared:  0.4231,    Adjusted R-squared:  0.403 
F-statistic: 21.02 on 3 and 86 DF,  p-value: 2.633e-10
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.43216 -0.70990 -0.09256  0.81013  2.30328 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.343e+00  2.875e-01   4.671 1.10e-05 ***
femininity  1.295e-02  3.976e-02   0.326    0.745    
damage      6.269e-05  9.518e-06   6.587 3.42e-09 ***
damage:post 3.267e-07  1.409e-05   0.023    0.982    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.042 on 86 degrees of freedom
Multiple R-squared:  0.4225,    Adjusted R-squared:  0.4023 
F-statistic: 20.97 on 3 and 86 DF,  p-value: 2.758e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3747 -0.7024 -0.1026  0.8234  2.3185 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.314e+00  3.241e-01   4.056  0.00011 ***
femininity         1.719e-02  4.580e-02   0.375  0.70837    
damage             6.655e-05  2.214e-05   3.005  0.00348 ** 
femininity:damage -5.301e-07  2.933e-06  -0.181  0.85699    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared:  0.4227,    Adjusted R-squared:  0.4025 
F-statistic: 20.99 on 3 and 86 DF,  p-value: 2.715e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3286 -0.7361 -0.0939  0.8221  2.3125 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.328e+00  3.301e-01   4.021 0.000125 ***
femininity         1.466e-02  4.714e-02   0.311 0.756538    
damage            -1.589e-04  8.979e-04  -0.177 0.859993    
femininity:damage  1.688e-07  4.055e-06   0.042 0.966892    
damage:year        1.116e-07  4.444e-07   0.251 0.802338    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.047 on 85 degrees of freedom
Multiple R-squared:  0.4231,    Adjusted R-squared:  0.396 
F-statistic: 15.58 on 4 and 85 DF,  p-value: 1.334e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4012 -0.7154 -0.1066  0.8262  2.3062 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.309e+00  3.290e-01   3.979 0.000145 ***
femininity         1.830e-02  4.691e-02   0.390 0.697504    
damage             6.966e-05  3.343e-05   2.084 0.040171 *  
femininity:damage -8.493e-07  3.901e-06  -0.218 0.828181    
damage:post       -2.343e-06  1.874e-05  -0.125 0.900800    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.047 on 85 degrees of freedom
Multiple R-squared:  0.4228,    Adjusted R-squared:  0.3956 
F-statistic: 15.56 on 4 and 85 DF,  p-value: 1.365e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19112 -0.80430  0.03142  0.69795  2.41478 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.001973   0.388181  -2.581   0.0115 *  
femininity   0.005032   0.035536   0.142   0.8877    
damage       0.398856   0.042391   9.409 6.53e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9573 on 87 degrees of freedom
Multiple R-squared:  0.5064,    Adjusted R-squared:  0.495 
F-statistic: 44.62 on 2 and 87 DF,  p-value: 4.603e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21075 -0.78479  0.04251  0.67656  2.40419 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.9948799  0.3933026  -2.530   0.0132 *
femininity   0.0036717  0.0368970   0.100   0.9210  
damage       0.6034857  1.3810964   0.437   0.6632  
damage:year -0.0001032  0.0006959  -0.148   0.8825  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9628 on 86 degrees of freedom
Multiple R-squared:  0.5065,    Adjusted R-squared:  0.4893 
F-statistic: 29.42 on 3 and 86 DF,  p-value: 3.48e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.23251 -0.75751  0.05769  0.65333  2.36262 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.976968   0.394507  -2.476   0.0152 *  
femininity   0.001121   0.036890   0.030   0.9758    
damage       0.404888   0.044929   9.012 4.66e-14 ***
damage:post -0.011350   0.026908  -0.422   0.6742    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9619 on 86 degrees of freedom
Multiple R-squared:  0.5074,    Adjusted R-squared:  0.4902 
F-statistic: 29.53 on 3 and 86 DF,  p-value: 3.222e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19768 -0.73660  0.01623  0.65071  2.31058 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.36880    0.77660  -0.475  0.63607   
femininity        -0.09931    0.11638  -0.853  0.39587   
damage             0.31201    0.10152   3.073  0.00283 **
femininity:damage  0.01415    0.01502   0.942  0.34906   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.958 on 86 degrees of freedom
Multiple R-squared:  0.5114,    Adjusted R-squared:  0.4944 
F-statistic:    30 on 3 and 86 DF,  p-value: 2.274e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19753 -0.73663  0.01632  0.65083  2.31064 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -3.687e-01  7.835e-01  -0.471    0.639
femininity        -9.932e-02  1.174e-01  -0.846    0.400
damage             3.104e-01  1.418e+00   0.219    0.827
femininity:damage  1.415e-02  1.531e-02   0.924    0.358
damage:year        8.204e-07  7.055e-04   0.001    0.999

Residual standard error: 0.9636 on 85 degrees of freedom
Multiple R-squared:  0.5114,    Adjusted R-squared:  0.4884 
F-statistic: 22.24 on 4 and 85 DF,  p-value: 1.371e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22726 -0.72395  0.01475  0.63232  2.27738 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.377652   0.781291  -0.483   0.6301   
femininity        -0.097698   0.117124  -0.834   0.4065   
damage             0.320054   0.105490   3.034   0.0032 **
femininity:damage  0.013545   0.015235   0.889   0.3765   
damage:post       -0.008187   0.027175  -0.301   0.7639   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9631 on 85 degrees of freedom
Multiple R-squared:  0.5119,    Adjusted R-squared:  0.489 
F-statistic: 22.29 on 4 and 85 DF,  p-value: 1.312e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.44225 -0.72109 -0.09184  0.83137  2.29590 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.350e+00  2.020e-01   6.684 2.12e-09 ***
femininity  1.135e-01  2.338e-01   0.486    0.628    
damage      6.274e-05  7.906e-06   7.936 6.66e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.035 on 87 degrees of freedom
Multiple R-squared:  0.4233,    Adjusted R-squared:   0.41 
F-statistic: 31.93 on 2 and 87 DF,  p-value: 3.997e-11
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.30882 -0.75421 -0.09351  0.84762  2.31573 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.332e+00  2.100e-01   6.341 1.02e-08 ***
femininity   1.330e-01  2.417e-01   0.550    0.584    
damage      -1.538e-04  6.332e-04  -0.243    0.809    
damage:year  1.096e-07  3.205e-07   0.342    0.733    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.04 on 86 degrees of freedom
Multiple R-squared:  0.4241,    Adjusted R-squared:  0.404 
F-statistic: 21.11 on 3 and 86 DF,  p-value: 2.45e-10
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.41779 -0.72513 -0.09213  0.83375  2.30471 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.347e+00  2.104e-01   6.401 7.81e-09 ***
femininity  1.170e-01  2.421e-01   0.483    0.630    
damage      6.243e-05  9.509e-06   6.565 3.77e-09 ***
damage:post 8.492e-07  1.408e-05   0.060    0.952    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared:  0.4233,    Adjusted R-squared:  0.4032 
F-statistic: 21.04 on 3 and 86 DF,  p-value: 2.59e-10
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.46187 -0.72457 -0.08919  0.82753  2.29048 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.357e+00  2.289e-01   5.926 6.26e-08 ***
femininity        1.044e-01  2.792e-01   0.374 0.709279    
damage            6.193e-05  1.558e-05   3.976 0.000146 ***
femininity:damage 1.096e-06  1.811e-05   0.060 0.951906    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared:  0.4233,    Adjusted R-squared:  0.4032 
F-statistic: 21.04 on 3 and 86 DF,  p-value: 2.59e-10
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.34291 -0.75363 -0.07312  0.83506  2.28990 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.367e+00  2.308e-01   5.922 6.56e-08 ***
femininity         7.625e-02  2.859e-01   0.267    0.790    
damage            -3.690e-04  8.557e-04  -0.431    0.667    
femininity:damage  9.078e-06  2.413e-05   0.376    0.708    
damage:year        2.152e-07  4.271e-07   0.504    0.616    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.045 on 85 degrees of freedom
Multiple R-squared:  0.425, Adjusted R-squared:  0.398 
F-statistic: 15.71 on 4 and 85 DF,  p-value: 1.162e-09
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.42897 -0.74152 -0.08542  0.82527  2.30515 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.359e+00  2.308e-01   5.887 7.61e-08 ***
femininity        9.824e-02  2.850e-01   0.345   0.7311    
damage            5.969e-05  2.369e-05   2.520   0.0136 *  
femininity:damage 2.977e-06  2.352e-05   0.127   0.8996    
damage:post       2.312e-06  1.827e-05   0.127   0.8996    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.047 on 85 degrees of freedom
Multiple R-squared:  0.4234,    Adjusted R-squared:  0.3963 
F-statistic: 15.61 on 4 and 85 DF,  p-value: 1.304e-09
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.17916 -0.79367  0.01258  0.69950  2.42938 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.96594    0.34808  -2.775  0.00675 ** 
femininity  -0.01366    0.21735  -0.063  0.95004    
damage       0.39961    0.04255   9.391 7.09e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9574 on 87 degrees of freedom
Multiple R-squared:  0.5063,    Adjusted R-squared:  0.4949 
F-statistic: 44.61 on 2 and 87 DF,  p-value: 4.641e-14
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.20537 -0.78596  0.02543  0.67954  2.41385 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.9600444  0.3511835  -2.734   0.0076 **
femininity  -0.0272028  0.2282872  -0.119   0.9054   
damage       0.6865984  1.3978529   0.491   0.6245   
damage:year -0.0001446  0.0007040  -0.205   0.8377   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9627 on 86 degrees of freedom
Multiple R-squared:  0.5065,    Adjusted R-squared:  0.4893 
F-statistic: 29.42 on 3 and 86 DF,  p-value: 3.473e-13
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22406 -0.75578  0.03644  0.66075  2.36687 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.94499    0.35224  -2.683  0.00875 ** 
femininity  -0.04886    0.22991  -0.213  0.83222    
damage       0.40709    0.04540   8.966 5.78e-14 ***
damage:post -0.01338    0.02742  -0.488  0.62675    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9616 on 86 degrees of freedom
Multiple R-squared:  0.5076,    Adjusted R-squared:  0.4905 
F-statistic: 29.56 on 3 and 86 DF,  p-value: 3.152e-13
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18995 -0.69177  0.03576  0.57306  2.33223 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54721    0.51557  -1.061    0.291    
femininity        -0.71166    0.67075  -1.061    0.292    
damage             0.34007    0.06882   4.941 3.79e-06 ***
femininity:damage  0.09623    0.08750   1.100    0.274    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9563 on 86 degrees of freedom
Multiple R-squared:  0.5131,    Adjusted R-squared:  0.4961 
F-statistic: 30.21 on 3 and 86 DF,  p-value: 1.957e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19527 -0.69063  0.03283  0.57389  2.32960 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -5.484e-01  5.194e-01  -1.056    0.294
femininity        -7.104e-01  6.754e-01  -1.052    0.296
damage             3.994e-01  1.422e+00   0.281    0.779
femininity:damage  9.568e-02  8.902e-02   1.075    0.286
damage:year       -2.972e-05  7.115e-04  -0.042    0.967

Residual standard error: 0.9619 on 85 degrees of freedom
Multiple R-squared:  0.5131,    Adjusted R-squared:  0.4902 
F-statistic:  22.4 on 4 and 85 DF,  p-value: 1.183e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22264 -0.69306  0.03572  0.57512  2.29001 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.548558   0.518221  -1.059    0.293    
femininity        -0.709614   0.674198  -1.053    0.296    
damage             0.347975   0.072627   4.791 6.96e-06 ***
femininity:damage  0.092372   0.088607   1.042    0.300    
damage:post       -0.009873   0.027614  -0.358    0.722    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9612 on 85 degrees of freedom
Multiple R-squared:  0.5139,    Adjusted R-squared:  0.491 
F-statistic: 22.46 on 4 and 85 DF,  p-value: 1.112e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.14976 -0.73101 -0.05897  0.81375  2.11335 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.208e+00  2.683e-01   4.504 2.09e-05 ***
femininity  2.055e-02  3.692e-02   0.556    0.579    
damage      7.666e-05  8.944e-06   8.571 3.68e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9933 on 86 degrees of freedom
Multiple R-squared:  0.462, Adjusted R-squared:  0.4495 
F-statistic: 36.93 on 2 and 86 DF,  p-value: 2.643e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.85544 -0.68736 -0.01766  0.77156  2.14403 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.272e+00  2.745e-01   4.635 1.28e-05 ***
femininity   1.090e-02  3.795e-02   0.287    0.775    
damage       8.084e-04  6.769e-04   1.194    0.236    
damage:year -3.691e-07  3.414e-07  -1.081    0.283    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9923 on 85 degrees of freedom
Multiple R-squared:  0.4693,    Adjusted R-squared:  0.4506 
F-statistic: 25.06 on 3 and 85 DF,  p-value: 1.038e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.77676 -0.67869 -0.01185  0.77964  2.13707 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.295e+00  2.726e-01   4.749 8.20e-06 ***
femininity   7.509e-03  3.769e-02   0.199    0.843    
damage       8.858e-05  1.195e-05   7.415 8.37e-11 ***
damage:post -2.240e-05  1.501e-05  -1.492    0.139    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9863 on 85 degrees of freedom
Multiple R-squared:  0.4758,    Adjusted R-squared:  0.4573 
F-statistic: 25.72 on 3 and 85 DF,  p-value: 6.222e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.33340 -0.68060  0.00043  0.78630  2.11302 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.387e+00  3.094e-01   4.484 2.27e-05 ***
femininity        -7.930e-03  4.433e-02  -0.179   0.8584    
damage             5.412e-05  2.145e-05   2.524   0.0135 *  
femininity:damage  3.559e-06  3.080e-06   1.156   0.2511    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9913 on 85 degrees of freedom
Multiple R-squared:  0.4704,    Adjusted R-squared:  0.4517 
F-statistic: 25.16 on 3 and 85 DF,  p-value: 9.576e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.11242 -0.67395  0.00762  0.78451  2.13007 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.366e+00  3.142e-01   4.346 3.86e-05 ***
femininity        -4.205e-03  4.524e-02  -0.093    0.926    
damage             4.655e-04  8.767e-04   0.531    0.597    
femininity:damage  2.427e-06  3.923e-06   0.619    0.538    
damage:year       -2.039e-07  4.344e-07  -0.469    0.640    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9959 on 84 degrees of freedom
Multiple R-squared:  0.4718,    Adjusted R-squared:  0.4466 
F-statistic: 18.75 on 4 and 84 DF,  p-value: 4.76e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.91453 -0.67436  0.01213  0.78673  2.13278 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.350e+00  3.116e-01   4.333 4.06e-05 ***
femininity        -1.423e-03  4.480e-02  -0.032   0.9747    
damage             7.761e-05  3.173e-05   2.446   0.0165 *  
femininity:damage  1.402e-06  3.755e-06   0.373   0.7098    
damage:post       -1.847e-05  1.840e-05  -1.004   0.3181    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9913 on 84 degrees of freedom
Multiple R-squared:  0.4766,    Adjusted R-squared:  0.4517 
F-statistic: 19.13 on 4 and 84 DF,  p-value: 3.25e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18319 -0.80666  0.02116  0.70095  2.43082 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.97661    0.39467  -2.474   0.0153 *  
femininity   0.00383    0.03582   0.107   0.9151    
damage       0.39586    0.04319   9.167 2.25e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9619 on 86 degrees of freedom
Multiple R-squared:  0.4955,    Adjusted R-squared:  0.4838 
F-statistic: 42.23 on 2 and 86 DF,  p-value: 1.673e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19434 -0.79570  0.02475  0.69166  2.42442 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.9734750  0.3988579  -2.441   0.0167 *
femininity   0.0031126  0.0371051   0.084   0.9333  
damage       0.5096226  1.4076632   0.362   0.7182  
damage:year -0.0000573  0.0007087  -0.081   0.9357  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9675 on 85 degrees of freedom
Multiple R-squared:  0.4955,    Adjusted R-squared:  0.4777 
F-statistic: 27.83 on 3 and 85 DF,  p-value: 1.24e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22032 -0.76742  0.04597  0.66709  2.38324 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.9580602  0.3999732  -2.395   0.0188 *  
femininity   0.0005726  0.0371091   0.015   0.9877    
damage       0.4015077  0.0461212   8.705 2.13e-13 ***
damage:post -0.0099008  0.0273423  -0.362   0.7182    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9668 on 85 degrees of freedom
Multiple R-squared:  0.4963,    Adjusted R-squared:  0.4785 
F-statistic: 27.91 on 3 and 85 DF,  p-value: 1.166e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.19218 -0.74921  0.00473  0.69324  2.32605 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.38241    0.78243  -0.489  0.62628   
femininity        -0.09512    0.11803  -0.806  0.42257   
damage             0.31420    0.10239   3.069  0.00288 **
femininity:damage  0.01347    0.01531   0.880  0.38139   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9632 on 85 degrees of freedom
Multiple R-squared:    0.5, Adjusted R-squared:  0.4824 
F-statistic: 28.34 on 3 and 85 DF,  p-value: 8.501e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18677 -0.74892  0.00674  0.69932  2.32853 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -3.803e-01  7.890e-01  -0.482    0.631
femininity        -9.538e-02  1.189e-01  -0.802    0.425
damage             2.579e-01  1.439e+00   0.179    0.858
femininity:damage  1.355e-02  1.555e-02   0.872    0.386
damage:year        2.809e-05  7.164e-04   0.039    0.969

Residual standard error: 0.9689 on 84 degrees of freedom
Multiple R-squared:  0.5001,    Adjusted R-squared:  0.4762 
F-statistic:    21 on 4 and 84 DF,  p-value: 4.98e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21948 -0.73492  0.00601  0.66357  2.29420 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -0.388623   0.787083  -0.494   0.6228   
femininity        -0.094207   0.118730  -0.793   0.4298   
damage             0.321146   0.106185   3.024   0.0033 **
femininity:damage  0.013017   0.015487   0.841   0.4030   
damage:post       -0.007362   0.027556  -0.267   0.7900   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9685 on 84 degrees of freedom
Multiple R-squared:  0.5005,    Adjusted R-squared:  0.4767 
F-statistic: 21.04 on 4 and 84 DF,  p-value: 4.813e-12
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.15532 -0.72737 -0.04954  0.81079  2.12410 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.241e+00  1.973e-01   6.290 1.27e-08 ***
femininity  1.472e-01  2.245e-01   0.656    0.514    
damage      7.659e-05  8.939e-06   8.567 3.74e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9926 on 86 degrees of freedom
Multiple R-squared:  0.4628,    Adjusted R-squared:  0.4503 
F-statistic: 37.04 on 2 and 86 DF,  p-value: 2.49e-12
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.86798 -0.68709 -0.01967  0.75271  2.15566 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.282e+00  2.009e-01   6.380 8.87e-09 ***
femininity   8.926e-02  2.310e-01   0.386    0.700    
damage       7.920e-04  6.770e-04   1.170    0.245    
damage:year -3.608e-07  3.414e-07  -1.057    0.294    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9919 on 85 degrees of freedom
Multiple R-squared:  0.4698,    Adjusted R-squared:  0.451 
F-statistic:  25.1 on 3 and 85 DF,  p-value: 1.005e-11
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.78785 -0.68656 -0.01926  0.76515  2.14785 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.299e+00  1.999e-01   6.497 5.30e-09 ***
femininity   6.580e-02  2.299e-01   0.286    0.775    
damage       8.836e-05  1.197e-05   7.380 9.85e-11 ***
damage:post -2.206e-05  1.505e-05  -1.466    0.146    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.986 on 85 degrees of freedom
Multiple R-squared:  0.476, Adjusted R-squared:  0.4575 
F-statistic: 25.74 on 3 and 85 DF,  p-value: 6.093e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.36341 -0.66899  0.01503  0.79200  2.12445 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.357e+00  2.177e-01   6.233  1.7e-08 ***
femininity        -3.794e-02  2.692e-01  -0.141    0.888    
damage             6.193e-05  1.481e-05   4.182  7.0e-05 ***
femininity:damage  2.297e-05  1.854e-05   1.239    0.219    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9895 on 85 degrees of freedom
Multiple R-squared:  0.4723,    Adjusted R-squared:  0.4537 
F-statistic: 25.36 on 3 and 85 DF,  p-value: 8.202e-12
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.17707 -0.66814  0.00776  0.78772  2.13935 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.348e+00  2.197e-01   6.137 2.66e-08 ***
femininity        -2.100e-02  2.737e-01  -0.077    0.939    
damage             4.050e-04  8.503e-04   0.476    0.635    
femininity:damage  1.745e-05  2.311e-05   0.755    0.452    
damage:year       -1.713e-07  4.244e-07  -0.404    0.688    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9944 on 84 degrees of freedom
Multiple R-squared:  0.4733,    Adjusted R-squared:  0.4483 
F-statistic: 18.87 on 4 and 84 DF,  p-value: 4.208e-11
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.97716 -0.71205  0.01397  0.78739  2.14245 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.342e+00  2.184e-01   6.146 2.56e-08 ***
femininity        -6.723e-03  2.715e-01  -0.025  0.98030    
damage             7.834e-05  2.311e-05   3.390  0.00107 ** 
femininity:damage  1.137e-05  2.239e-05   0.508  0.61303    
damage:post       -1.688e-05  1.823e-05  -0.925  0.35736    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9903 on 84 degrees of freedom
Multiple R-squared:  0.4776,    Adjusted R-squared:  0.4528 
F-statistic:  19.2 on 4 and 84 DF,  p-value: 3.007e-11
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.17125 -0.81223 -0.00544  0.71008  2.44455 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.94425    0.35326  -2.673  0.00899 ** 
femininity  -0.01888    0.21870  -0.086  0.93142    
damage       0.39652    0.04334   9.150 2.44e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9619 on 86 degrees of freedom
Multiple R-squared:  0.4955,    Adjusted R-squared:  0.4837 
F-statistic: 42.23 on 2 and 86 DF,  p-value: 1.676e-13
fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18908 -0.81029  0.00002  0.69381  2.43344 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -9.415e-01  3.559e-01  -2.645  0.00972 **
femininity  -2.760e-02  2.294e-01  -0.120  0.90454   
damage       5.873e-01  1.426e+00   0.412  0.68153   
damage:year -9.604e-05  7.177e-04  -0.134  0.89386   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9675 on 85 degrees of freedom
Multiple R-squared:  0.4956,    Adjusted R-squared:  0.4778 
F-statistic: 27.84 on 3 and 85 DF,  p-value: 1.236e-12
fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21218 -0.77634  0.01641  0.67109  2.38694 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.92900    0.35677  -2.604   0.0109 *  
femininity  -0.04923    0.23109  -0.213   0.8318    
damage       0.40361    0.04663   8.655  2.7e-13 ***
damage:post -0.01184    0.02789  -0.425   0.6722    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9665 on 85 degrees of freedom
Multiple R-squared:  0.4965,    Adjusted R-squared:  0.4788 
F-statistic: 27.94 on 3 and 85 DF,  p-value: 1.141e-12
fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18416 -0.70048  0.01967  0.57862  2.34603 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.54721    0.51833  -1.056    0.294    
femininity        -0.69052    0.67815  -1.018    0.311    
damage             0.34007    0.06919   4.915 4.27e-06 ***
femininity:damage  0.09283    0.08873   1.046    0.298    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9614 on 85 degrees of freedom
Multiple R-squared:  0.5019,    Adjusted R-squared:  0.4843 
F-statistic: 28.55 on 3 and 85 DF,  p-value: 7.282e-13
fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18379 -0.70048  0.01985  0.57851  2.34623 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)       -5.471e-01  5.223e-01  -1.048    0.298
femininity        -6.906e-01  6.825e-01  -1.012    0.315
damage             3.361e-01  1.446e+00   0.232    0.817
femininity:damage  9.286e-02  9.003e-02   1.031    0.305
damage:year        1.995e-06  7.236e-04   0.003    0.998

Residual standard error: 0.9671 on 84 degrees of freedom
Multiple R-squared:  0.5019,    Adjusted R-squared:  0.4782 
F-statistic: 21.16 on 4 and 84 DF,  p-value: 4.285e-12
fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)

Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.21457 -0.70756  0.02929  0.58329  2.30581 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       -0.548427   0.521109  -1.052    0.296    
femininity        -0.691889   0.681779  -1.015    0.313    
damage             0.347208   0.073099   4.750  8.3e-06 ***
femininity:damage  0.089858   0.089685   1.002    0.319    
damage:post       -0.008916   0.028039  -0.318    0.751    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9665 on 84 degrees of freedom
Multiple R-squared:  0.5025,    Adjusted R-squared:  0.4788 
F-statistic: 21.21 on 4 and 84 DF,  p-value: 4.079e-12
fit <- lm(log(death + 1) ~ 
          branch(main_interaction,
              "main" ~ femininity + damage,
              # "with_damage_and_z3" ~ femininity * damage + femininity * z3,
              # "with_damage_and_zcat" ~ femininity * damage + femininity * zcat,
              # "with_damage_and_zwind" ~ femininity * damage + femininity * zwind,
              # "with_damage_and_zpressure" ~ femininity * damage + femininity * zpressure,
              "with_damage" ~ femininity * damage
          ) + branch(control_year, "none" ~ NULL, "year_x_damage" ~ year:damage, "post1979_x_damage" ~ post:damage), 
          data = df)

summary(fit)

We first visualise the model’s coefficients as confidence intervals:

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
    ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
    color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")

broom::tidy(fit, conf.int = TRUE) %>%
  ggplot() +
  geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high, color = (p.value < 0.05))) +
  theme_minimal() +
  labs(y ="Coefficient", x = "Mean point estimate and 95% Confidence Interval")

Next, we predict the expected number of deaths and a 50% prediction interval as a function of the femininity of the name

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
    broom::augment(fit, newdata = .) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
    scale_fill_brewer() + theme_minimal()

data_grid(df, femininity, damage, nesting(post, year)) %>%
  broom::augment(fit, newdata = .) %>%
  mutate(.fitted = exp(.fitted) - 1) %>%
  ggplot() +
  ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
  scale_fill_brewer() +
  theme_minimal()

Execution and Results

Next, we attempt to make sense of the multiverse analysis as a whole, using a specification curve. We first create a new datastructure new_hurricane_data, and estimate the average expected number of deaths and standard error. To make comparable point estimates for the continuous and discrete measures of femininity, we compute the average value of the former for the two possible values of the latter, and compute as the effect size the difference in predicted deaths for both values. Thus, mean_deaths are marginal effects computed at sample means.

masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
    nesting(post, year)) %>%
    mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
    filter(term == "femininity")
expectation = new_hurricane_data %>%
    broom::augment(fit, newdata = ., se_fit = TRUE) %>%
    mutate(.fitted = exp(.fitted) - 1) %>%
    mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
    group_by(femininity) %>%
    summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
    pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
    mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
    select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)
# used for multiverse vis
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, 
                               femininity = masfem_levels, 
                               damage = c(2000, 4000), 
                               # nesting(zcat, zwind, zpressure, z3), 
                               nesting(post, year)) %>%
    mutate(
        damage = branch(damage_transform,
          "no_transform" ~ identity(damage),
          "log_transform" ~ log(damage)
        )
    )

model.coef = broom::tidy(fit, conf.int = TRUE) %>%
  filter(term == "femininity") 

# aggregate fitted effect of female storm name
expectation = new_hurricane_data %>%
  broom::augment(fit, newdata = ., se_fit = TRUE) %>%
  mutate(.fitted = exp(.fitted) - 1) %>%
  mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
  group_by(femininity) %>%
  summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
  pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
  mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
  select(mean_deaths, .se)

deg_freedom.model = df.residual(fit)

After we’ve specified our multiverse analysis, we would like to execute the entire multiverse, and view the results.

execute_multiverse(M)

Below, we plot the mean difference point estimate for expected deaths when a hurricane has a more feminine name, for each unique analysis path. We find that based on these arbitrary specifications of the multiverse, there is perhaps no relation between femininity of the name of a hurricane and the number of deaths that it causes, as some models predict a lower number of deaths, and some predict much higher.

data.spec_curve = extract_variables(M, expectation, model.coef, deg_freedom.model) %>%
  unnest(c(expectation, model.coef)) %>%
  select( .universe, !! names(parameters(M)), mean_deaths, estimate, .se, p.value, deg_freedom.model) %>%
  arrange( mean_deaths ) %>%
  mutate( 
    .universe = 1:nrow(.),
    effect = ifelse(p.value < 0.05, ifelse(estimate < 0, "negative", "positive"), "not significant")
  )

p1 <- data.spec_curve %>%
  gather( "parameter_name", "parameter_option", !! names(parameters(M)) ) %>%
  mutate( parameter_name = factor(stringr::str_replace(parameter_name, "_", "\n"))  ) %>%
  ggplot() +
  geom_point( aes(x = .universe, y = parameter_option, color = effect), size = 1 ) +
  labs( x = "universe #", y = "option included in the analysis specification") + 
  facet_grid(parameter_name ~ ., space="free_y", scales="free_y", switch="y")+ 
  scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB")) +
  theme_minimal() +
  theme(strip.placement = "outside",
        strip.background = element_rect(fill=NA,colour=NA),
        panel.spacing.x=unit(0.15,"cm"), 
        strip.text.y = element_text(angle = 180, face="bold", size=10), 
        panel.spacing = unit(0.25, "lines")
      )

p2 <- data.spec_curve %>%
  mutate(
    conf.low = purrr::pmap_dbl(list(mean_deaths, .se, deg_freedom.model), ~ gamlss.dist::qTF(0.025, ..1, ..2, ..3)),
    conf.high = purrr::pmap_dbl(list(mean_deaths, .se, deg_freedom.model), ~ gamlss.dist::qTF(0.975, ..1, ..2, ..3))
  ) %>%
  ggplot() +
  ggdist::geom_pointinterval(aes(x = .universe, y = mean_deaths, ymin = conf.low, ymax = conf.high, color = effect)) +
  labs(x = "", y = "effect size") + 
  theme_minimal() +
  scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB"))

cowplot::plot_grid(p2, p1, axis = "bltr",  align = "v", ncol = 1, rel_heights = c(1, 3))